From 6e3a5e7c9338d93d88c4af34d91db20c9a0dad2e Mon Sep 17 00:00:00 2001 From: olivroy Date: Wed, 6 Dec 2023 09:25:26 -0500 Subject: [PATCH 1/4] Update vignette title to match --- vignettes/forecasting_with_cleaned_anomalies.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vignettes/forecasting_with_cleaned_anomalies.Rmd b/vignettes/forecasting_with_cleaned_anomalies.Rmd index a5c95e8..3b6ccca 100644 --- a/vignettes/forecasting_with_cleaned_anomalies.Rmd +++ b/vignettes/forecasting_with_cleaned_anomalies.Rmd @@ -4,7 +4,7 @@ author: "Business Science" date: "`r Sys.Date()`" output: rmarkdown::html_vignette vignette: > - %\VignetteIndexEntry{Forecasting with Cleaned Anomalies} + %\VignetteIndexEntry{Reduce Forecast Error with Cleaned Anomalies} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- From 4edeaedeed713d96e5c74d4ca5d62cc4e6ecc0b4 Mon Sep 17 00:00:00 2001 From: olivroy Date: Wed, 6 Dec 2023 09:27:15 -0500 Subject: [PATCH 2/4] Update docs To see the diff before deletion --- docs/articles/anomalize_methods.html | 6 ++- .../figure-html/unnamed-chunk-3-1.png | Bin 334409 -> 334914 bytes .../figure-html/unnamed-chunk-3-2.png | Bin 320366 -> 322818 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 46255 -> 46309 bytes .../figure-html/unnamed-chunk-5-1.png | Bin 55550 -> 55728 bytes .../figure-html/unnamed-chunk-5-2.png | Bin 59126 -> 59477 bytes .../articles/anomalize_quick_start_guide.html | 2 +- .../figure-html/unnamed-chunk-11-1.png | Bin 265784 -> 266067 bytes .../figure-html/unnamed-chunk-13-1.png | Bin 180920 -> 181719 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 200273 -> 201372 bytes .../figure-html/unnamed-chunk-14-2.png | Bin 172534 -> 173421 bytes .../figure-html/unnamed-chunk-15-1.png | Bin 257547 -> 257442 bytes .../figure-html/unnamed-chunk-15-2.png | Bin 210256 -> 211092 bytes .../figure-html/unnamed-chunk-4-1.png | Bin 797290 -> 799428 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 252861 -> 253903 bytes .../figure-html/unnamed-chunk-9-1.png | Bin 308194 -> 308754 bytes .../figure-html/unnamed-chunk-9-2.png | Bin 316105 -> 317155 bytes .../forecasting_with_cleaned_anomalies.html | 9 ++-- .../figure-html/unnamed-chunk-3-1.png | Bin 600462 -> 604905 bytes docs/index.html | 41 +++++++++++------- docs/news/index.html | 2 +- docs/pkgdown.yml | 4 +- docs/reference/decompose_methods.html | 7 +-- .../figures/README-tidyverse_anoms_1-1.png | Bin 1004690 -> 1004701 bytes .../figures/README-unnamed-chunk-3-1.png | Bin 377384 -> 377395 bytes .../plot_anomaly_decomposition-1.png | Bin 219748 -> 219796 bytes docs/reference/prep_tbl_time.html | 5 ++- 27 files changed, 47 insertions(+), 29 deletions(-) diff --git a/docs/articles/anomalize_methods.html b/docs/articles/anomalize_methods.html index 3fc9014..20ab582 100644 --- a/docs/articles/anomalize_methods.html +++ b/docs/articles/anomalize_methods.html @@ -97,7 +97,7 @@

Anomalize Methods

Business Science

-

2023-10-31

+

2023-12-06

Source: vignettes/anomalize_methods.Rmd @@ -280,6 +280,8 @@

1.C. Comparison anomalize(remainder) %>% plot_anomaly_decomposition() + ggtitle("STL Decomposition") +#> Converting from tbl_df to tbl_time. +#> Auto-index message: index = date #> frequency = 7 days #> trend = 91 days #> Registered S3 method overwritten by 'quantmod': @@ -295,6 +297,8 @@

1.C. 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  • Business Science

    -

    2023-10-31

    +

    2023-12-06

    Source:
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    - +
    +

    Dev status

    +
      +
    • R-CMD-check
    • +
    • Lifecycle Status
    • +
    • Coverage status
    • +
    • CRAN_Status_Badge
    • +
    +
    diff --git a/docs/news/index.html b/docs/news/index.html index 304abe3..c88aea3 100644 --- a/docs/news/index.html +++ b/docs/news/index.html @@ -73,7 +73,7 @@

    Changelog

    - +

    Prepare for supercession by timetk. Note that anomalize R package will be maintained for backwards compatibility. Users may wish to add these 2 lines of code to existing codebases that use the legacy anomalize R package:

     
    diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
    index a1ec90e..fe99a4a 100644
    --- a/docs/pkgdown.yml
    +++ b/docs/pkgdown.yml
    @@ -1,9 +1,9 @@
    -pandoc: 3.1.1
    +pandoc: 3.1.8
     pkgdown: 2.0.7
     pkgdown_sha: ~
     articles:
       anomalize_methods: anomalize_methods.html
       anomalize_quick_start_guide: anomalize_quick_start_guide.html
       forecasting_with_cleaned_anomalies: forecasting_with_cleaned_anomalies.html
    -last_built: 2023-10-31T21:48Z
    +last_built: 2023-12-06T14:24Z
     
    diff --git a/docs/reference/decompose_methods.html b/docs/reference/decompose_methods.html
    index 274d31b..7312324 100644
    --- a/docs/reference/decompose_methods.html
    +++ b/docs/reference/decompose_methods.html
    @@ -137,12 +137,13 @@ 

    See also

    Examples

    
     library(dplyr)
    +#> Warning: package 'dplyr' was built under R version 4.3.2
     #> 
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    -#> The following objects are masked from ‘package:stats’:
    +#> Attaching package: 'dplyr'
    +#> The following objects are masked from 'package:stats':
     #> 
     #>     filter, lag
    -#> The following objects are masked from ‘package:base’:
    +#> The following objects are masked from 'package:base':
     #> 
     #>     intersect, setdiff, setequal, union
     
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    Examples

    
     library(dplyr)
     library(tibbletime)
    +#> Warning: package 'tibbletime' was built under R version 4.3.2
     #> 
    -#> Attaching package: ‘tibbletime’
    -#> The following object is masked from ‘package:stats’:
    +#> Attaching package: 'tibbletime'
    +#> The following object is masked from 'package:stats':
     #> 
     #>     filter
     
    
    From 11fd9a6a73b281a90824bb1a0d75c1d738ffd965 Mon Sep 17 00:00:00 2001
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    @@ -1,130 +0,0 @@
    -
    -
    -
    -
    -
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    -
    -Page not found (404) • anomalize
    -
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    -
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    - - - - -
    -
    - - -Content not found. Please use links in the navbar. - -
    - - - -
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    - -
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    -

    Site built with pkgdown 2.0.7.

    -
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    -
    - - - - - - - - diff --git a/docs/articles/anomalize_methods.html b/docs/articles/anomalize_methods.html deleted file mode 100644 index 20ab582..0000000 --- a/docs/articles/anomalize_methods.html +++ /dev/null @@ -1,529 +0,0 @@ - - - - - - - -Anomalize Methods • anomalize - - - - - - - - - - - - -
    -
    - - - - -
    -
    - - - - -

    Anomaly detection is critical to many disciplines, but possibly none -more important than in time series analysis. A time -series is the sequential set of values tracked over a time duration. The -definition we use for an anomaly is simple: an anomaly -is something that happens that (1) was unexpected or (2) was caused by -an abnormal event. Therefore, the problem we intend to solve with -anomalize is providing methods to accurately detect these -“anomalous” events.

    -

    The methods that anomalize uses can be separated into -two main tasks:

    -
      -
    1. Generating Time Series Analysis Remainders
    2. -
    3. Detecting Anomalies in the Remainders
    4. -
    -
    -

    1. Generating Time Series Analysis Remainders -

    -

    Anomaly detection is performed on remainders from a -time series analysis that have had removed both:

    -
      -
    • -Seasonal Components: Cyclic pattern usually -occurring on a daily cycle for minute or hour data or a weekly cycle for -daily data
    • -
    • -Trend Components: Longer term growth that happens -over many observations.
    • -
    -

    Therefore, the first objective is to generate remainders from a time -series. Some analysis techniques are better for this task then others, -and it’s probably not the ones you would think.

    -

    There are many ways that a time series can be deconstructed to -produce residuals. We have tried many including using ARIMA, Machine -Learning (Regression), Seasonal Decomposition, and so on. For anomaly -detection, we have seen the best performance using seasonal -decomposition. Most high performance machine learning -techniques perform poorly for anomaly detection because of -overfitting, which downplays the difference between the actual -value and the fitted value. This is not the objective of anomaly -detection wherein we need to highlight the anomaly. Seasonal -decomposition does very well for this task, removing the right features -(i.e. seasonal and trend components) while preserving the -characteristics of anomalies in the residuals.

    -

    The anomalize package implements two techniques for -seasonal decomposition:

    -
      -
    1. -STL: Seasonal Decomposition of Time Series by -Loess
    2. -
    3. -Twitter: Seasonal Decomposition of Time Series by -Median
    4. -
    -

    Each method has pros and cons.

    -
    -

    1.A. STL -

    -

    The STL method uses the stl() function from the -stats package. STL works very well in circumstances where a -long term trend is present. The Loess algorithm typically does a very -good job at detecting the trend. However, it circumstances when the -seasonal component is more dominant than the trend, Twitter tends to -perform better.

    -
    -
    -

    1.B. Twitter -

    -

    The Twitter method is a similar decomposition method to that used in -Twitter’s AnomalyDetection package. The Twitter method -works identically to STL for removing the seasonal component. The main -difference is in removing the trend, which is performed by removing the -median of the data rather than fitting a smoother. The median works well -when a long-term trend is less dominant that the short-term seasonal -component. This is because the smoother tends to overfit the -anomalies.

    -
    -
    -

    1.C. Comparison of STL and Twitter Decomposition Methods -

    -

    Load two libraries to perform the comparison.

    -
    -library(tidyverse)
    -library(anomalize)
    -
    -# NOTE: timetk now has anomaly detection built in, which 
    -#  will get the new functionality going forward.
    -
    -anomalize <- anomalize::anomalize
    -plot_anomalies <- anomalize::plot_anomalies
    -

    Collect data on the daily downloads of the lubridate -package. This comes from the data set, -tidyverse_cran_downloads that is part of -anomalize package.

    -
    -# Data on `lubridate` package daily downloads
    -lubridate_download_history <- tidyverse_cran_downloads %>%
    -    filter(package == "lubridate") %>%
    -    ungroup()
    -
    -# Output first 10 observations
    -lubridate_download_history %>%
    -    head(10) %>%
    -    knitr::kable()
    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    datecountpackage
    2017-01-01643lubridate
    2017-01-021350lubridate
    2017-01-032940lubridate
    2017-01-044269lubridate
    2017-01-053724lubridate
    2017-01-062326lubridate
    2017-01-071107lubridate
    2017-01-081058lubridate
    2017-01-092494lubridate
    2017-01-103237lubridate
    -

    We can visualize the differences between the two decomposition -methods.

    -
    -# STL Decomposition Method
    -p1 <- lubridate_download_history %>%
    -    time_decompose(count, 
    -                   method    = "stl",
    -                   frequency = "1 week",
    -                   trend     = "3 months") %>%
    -    anomalize(remainder) %>%
    -    plot_anomaly_decomposition() +
    -    ggtitle("STL Decomposition")
    -#> Converting from tbl_df to tbl_time.
    -#> Auto-index message: index = date
    -#> frequency = 7 days
    -#> trend = 91 days
    -#> Registered S3 method overwritten by 'quantmod':
    -#>   method            from
    -#>   as.zoo.data.frame zoo
    -
    -# Twitter Decomposition Method
    -p2 <- lubridate_download_history %>%
    -    time_decompose(count, 
    -                   method    = "twitter",
    -                   frequency = "1 week",
    -                   trend     = "3 months") %>%
    -    anomalize(remainder) %>%
    -    plot_anomaly_decomposition() +
    -    ggtitle("Twitter Decomposition")
    -#> Converting from tbl_df to tbl_time.
    -#> Auto-index message: index = date
    -#> frequency = 7 days
    -#> median_span = 85 days
    -
    -# Show plots
    -p1
    -p2
    -

    -

    We can see that the season components for both STL and Twitter -decomposition are exactly the same. The difference is the trend -component:

    -
      -
    • STL: The STL trend follows a smoothed Loess with a Loess trend -window at 91 days (as defined by trend = "3 months"). The -remainder of the decomposition is centered.

    • -
    • Twitter: The Twitter trend is a series of medians that are -removed. The median span logic is such that the medians are selected to -have equal distribution of observations. Because of this, the trend span -is 85 days, which is slightly less than the 91 days (or 3 -months).

    • -
    -
    -
    -

    1.D. Transformations -

    -

    In certain circumstances such as multiplicative trends in which the -residuals (remainders) have heteroskedastic properties, which is when -the variance changes as the time series sequence progresses (e.g. the -remainders fan out), it becomes difficult to detect anomalies in -especially in the low variance regions. Logarithmic or power -transformations can help in these situations. This is beyond the scope -of the methods and is not implemented in the current version of -anomalize. However, these transformations can be performed -on the incoming target and the output can be inverse-transformed.

    -
    -
    -
    -

    2. Detecting Anomalies in the Remainders -

    -

    Once a time series analysis is completed and the remainder has the -desired characteristics, the remainders can be analyzed. The challenge -is that anomalies are high leverage points that distort the -distribution. The anomalize package implements two methods -that are resistant to the high leverage points:

    -
      -
    1. -IQR: Inner Quartile Range
    2. -
    3. -GESD: Generalized Extreme Studentized Deviate -Test
    4. -
    -

    Both methods have pros and cons.

    -
    -

    2.A. IQR -

    -

    The IQR method is a similar method to that used in the -forecast package for anomaly removal within the -tsoutliers() function. It takes a distribution and uses the -25% and 75% inner quartile range to establish the distribution of the -remainder. Limits are set by default to a factor of 3X above and below -the inner quartile range, and any remainders beyond the limits are -considered anomalies.

    -

    The alpha parameter adjusts the 3X factor. By default, -alpha = 0.05 for consistency with the GESD method. An -alpha = 0.025, results in a 6X factor, expanding the limits -and making it more difficult for data to be an anomaly. Conversely, an -alpha = 0.10 contracts the limits to a factor of 1.5X -making it more easy for data to be an anomaly.

    -

    The IQR method does not depend on any loops and is therefore faster -and more easily scaled than the GESD method. However, it may not be as -accurate in detecting anomalies since the high leverage anomalies can -skew the centerline (median) of the IQR.

    -
    -
    -

    2.B. GESD -

    -

    The GESD method is used in Twitter’s AnomalyDetection -package. It involves an iterative evaluation of the Generalized Extreme -Studentized Deviate test, which progressively evaluates anomalies, -removing the worst offenders and recalculating the test statistic and -critical value. The critical values progressively contract as more high -leverage points are removed.

    -

    The alpha parameter adjusts the width of the critical -values. By default, alpha = 0.05.

    -

    The GESD method is iterative, and therefore more expensive that the -IQR method. The main benefit is that GESD is less resistant to high -leverage points since the distribution of the data is progressively -analyzed as anomalies are removed.

    -
    -
    -

    2.C Comparison of IQR and GESD Methods -

    -

    We can generate anomalous data to illustrate how each method work -compares to each other.

    -
    -# Generate anomalies
    -set.seed(100)
    -x <- rnorm(100)
    -idx_outliers    <- sample(100, size = 5)
    -x[idx_outliers] <- x[idx_outliers] + 10
    -
    -# Visualize simulated anomalies
    -qplot(1:length(x), x, 
    -      main = "Simulated Anomalies",
    -      xlab = "Index") 
    -

    -

    Two functions power anomalize(), which are -iqr() and gesd(). We can use these -intermediate functions to illustrate the anomaly detection -characteristics.

    -
    -# Analyze outliers: Outlier Report is available with verbose = TRUE
    -iqr_outliers <- iqr(x, alpha = 0.05, max_anoms = 0.2, verbose = TRUE)$outlier_report
    -
    -gesd_outliers <- gesd(x, alpha = 0.05, max_anoms = 0.2, verbose = TRUE)$outlier_report
    -
    -# ploting function for anomaly plots
    -ggsetup <- function(data) {
    -    data %>%
    -        ggplot(aes(rank, value, color = outlier)) +
    -        geom_point() +
    -        geom_line(aes(y = limit_upper), color = "red", linetype = 2) +
    -        geom_line(aes(y = limit_lower), color = "red", linetype = 2) +
    -        geom_text(aes(label = index), vjust = -1.25) +
    -        theme_bw() +
    -        scale_color_manual(values = c("No" = "#2c3e50", "Yes" = "#e31a1c")) +
    -        expand_limits(y = 13) +
    -        theme(legend.position = "bottom")
    -}
    -    
    -
    -# Visualize
    -p3 <- iqr_outliers %>% 
    -    ggsetup() +
    -    ggtitle("IQR: Top outliers sorted by rank") 
    -
    -p4 <- gesd_outliers %>% 
    -    ggsetup() +
    -    ggtitle("GESD: Top outliers sorted by rank") 
    -    
    -# Show plots
    -p3
    -p4
    -

    -

    We can see that the IQR limits don’t vary whereas the GESD limits get -more stringent as anomalies are removed from the data. As a result, the -GESD method tends to be more accurate in detecting anomalies at the -expense of incurring more processing time for the looped anomaly -removal. This expense is most noticeable with larger data sets (many -observations or many time series).

    -
    -
    -
    -

    3. Conclusion -

    -

    The anomalize package implements several useful and -accurate techniques for implementing anomaly detection. The user should -now have a better understanding of how the algorithms work along with -the strengths and weaknesses of each method.

    -
    - -
    -

    Interested in Learning Anomaly Detection? -

    -

    Business Science offers two 1-hour courses on Anomaly Detection:

    - -
    -
    - - - -
    - - - -
    - -
    -

    -

    Site built with pkgdown 2.0.7.

    -
    - -
    -
    - - - - - - - - diff --git a/docs/articles/anomalize_methods_files/accessible-code-block-0.0.1/empty-anchor.js b/docs/articles/anomalize_methods_files/accessible-code-block-0.0.1/empty-anchor.js deleted file mode 100644 index ca349fd..0000000 --- a/docs/articles/anomalize_methods_files/accessible-code-block-0.0.1/empty-anchor.js +++ /dev/null @@ -1,15 +0,0 @@ -// Hide empty tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) --> -// v0.0.1 -// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020. - -document.addEventListener('DOMContentLoaded', function() { - const codeList = document.getElementsByClassName("sourceCode"); - for (var i = 0; i < codeList.length; i++) { - var linkList = codeList[i].getElementsByTagName('a'); - for (var j = 0; j < linkList.length; j++) { - if (linkList[j].innerHTML === "") { - linkList[j].setAttribute('aria-hidden', 'true'); - } - } - } -}); 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a/docs/articles/anomalize_quick_start_guide_files/header-attrs-2.4/header-attrs.js b/docs/articles/anomalize_quick_start_guide_files/header-attrs-2.4/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/anomalize_quick_start_guide_files/header-attrs-2.4/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. 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a/docs/articles/forecasting_with_cleaned_anomalies_files/header-attrs-2.4/header-attrs.js b/docs/articles/forecasting_with_cleaned_anomalies_files/header-attrs-2.4/header-attrs.js deleted file mode 100644 index dd57d92..0000000 --- a/docs/articles/forecasting_with_cleaned_anomalies_files/header-attrs-2.4/header-attrs.js +++ /dev/null @@ -1,12 +0,0 @@ -// Pandoc 2.9 adds attributes on both header and div. We remove the former (to -// be compatible with the behavior of Pandoc < 2.8). -document.addEventListener('DOMContentLoaded', function(e) { - var hs = document.querySelectorAll("div.section[class*='level'] > :first-child"); - var i, h, a; - for (i = 0; i < hs.length; i++) { - h = hs[i]; - if (!/^h[1-6]$/i.test(h.tagName)) continue; // it should be a header h1-h6 - a = h.attributes; - while (a.length > 0) h.removeAttribute(a[0].name); - } -}); diff --git a/docs/articles/index.html b/docs/articles/index.html deleted file mode 100644 index 2e2cd9b..0000000 --- a/docs/articles/index.html +++ /dev/null @@ -1,105 +0,0 @@ - -Articles • anomalize - - -

      - - - - - - - - diff --git a/docs/authors.html b/docs/authors.html deleted file mode 100644 index 9c3db64..0000000 --- a/docs/authors.html +++ /dev/null @@ -1,127 +0,0 @@ - -Authors and Citation • anomalize - - -
      -
      - - - -
      -
      -
      - - - -
      • -

        Matt Dancho. Author, maintainer. -

        -
      • -
      • -

        Davis Vaughan. Author. -

        -
      • -
      -
      -
      -

      Citation

      - Source: DESCRIPTION -
      -
      - - -

      Dancho M, Vaughan D (2023). -anomalize: Tidy Anomaly Detection. -R package version 0.3.0, https://github.com/business-science/anomalize. -

      -
      @Manual{,
      -  title = {anomalize: Tidy Anomaly Detection},
      -  author = {Matt Dancho and Davis Vaughan},
      -  year = {2023},
      -  note = {R package version 0.3.0},
      -  url = {https://github.com/business-science/anomalize},
      -}
      - -
      - -
      - - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/bootstrap-toc.css b/docs/bootstrap-toc.css deleted file mode 100644 index 5a85941..0000000 --- a/docs/bootstrap-toc.css +++ /dev/null @@ -1,60 +0,0 @@ -/*! - * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) - * Copyright 2015 Aidan Feldman - * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ - -/* modified from https://github.com/twbs/bootstrap/blob/94b4076dd2efba9af71f0b18d4ee4b163aa9e0dd/docs/assets/css/src/docs.css#L548-L601 */ - -/* All levels of nav */ -nav[data-toggle='toc'] .nav > li > a { - display: block; - padding: 4px 20px; - font-size: 13px; - font-weight: 500; - color: #767676; -} -nav[data-toggle='toc'] .nav > li > a:hover, -nav[data-toggle='toc'] .nav > li > a:focus { - padding-left: 19px; - color: #563d7c; - text-decoration: none; - background-color: transparent; - border-left: 1px solid #563d7c; -} -nav[data-toggle='toc'] .nav > .active > a, -nav[data-toggle='toc'] .nav > .active:hover > a, -nav[data-toggle='toc'] .nav > .active:focus > a { - padding-left: 18px; - font-weight: bold; - color: #563d7c; - background-color: transparent; - border-left: 2px solid #563d7c; -} - -/* Nav: second level (shown on .active) */ -nav[data-toggle='toc'] .nav .nav { - display: none; /* Hide by default, but at >768px, show it */ - padding-bottom: 10px; -} -nav[data-toggle='toc'] .nav .nav > li > a { - padding-top: 1px; - padding-bottom: 1px; - padding-left: 30px; - font-size: 12px; - font-weight: normal; -} -nav[data-toggle='toc'] .nav .nav > li > a:hover, -nav[data-toggle='toc'] .nav .nav > li > a:focus { - padding-left: 29px; -} -nav[data-toggle='toc'] .nav .nav > .active > a, -nav[data-toggle='toc'] .nav .nav > .active:hover > a, -nav[data-toggle='toc'] .nav .nav > .active:focus > a { - padding-left: 28px; - font-weight: 500; -} - -/* from https://github.com/twbs/bootstrap/blob/e38f066d8c203c3e032da0ff23cd2d6098ee2dd6/docs/assets/css/src/docs.css#L631-L634 */ -nav[data-toggle='toc'] .nav > .active > ul { - display: block; -} diff --git a/docs/bootstrap-toc.js b/docs/bootstrap-toc.js deleted file mode 100644 index 1cdd573..0000000 --- a/docs/bootstrap-toc.js +++ /dev/null @@ -1,159 +0,0 @@ -/*! - * Bootstrap Table of Contents v0.4.1 (http://afeld.github.io/bootstrap-toc/) - * Copyright 2015 Aidan Feldman - * Licensed under MIT (https://github.com/afeld/bootstrap-toc/blob/gh-pages/LICENSE.md) */ -(function() { - 'use strict'; - - window.Toc = { - helpers: { - // return all matching elements in the set, or their descendants - findOrFilter: function($el, selector) { - // http://danielnouri.org/notes/2011/03/14/a-jquery-find-that-also-finds-the-root-element/ - // http://stackoverflow.com/a/12731439/358804 - var $descendants = $el.find(selector); - return $el.filter(selector).add($descendants).filter(':not([data-toc-skip])'); - }, - - generateUniqueIdBase: function(el) { - var text = $(el).text(); - var anchor = text.trim().toLowerCase().replace(/[^A-Za-z0-9]+/g, '-'); - return anchor || el.tagName.toLowerCase(); - }, - - generateUniqueId: function(el) { - var anchorBase = this.generateUniqueIdBase(el); - for (var i = 0; ; i++) { - var anchor = anchorBase; - if (i > 0) { - // add suffix - anchor += '-' + i; - } - // check if ID already exists - if (!document.getElementById(anchor)) { - return anchor; - } - } - }, - - generateAnchor: function(el) { - if (el.id) { - return el.id; - } else { - var anchor = this.generateUniqueId(el); - el.id = anchor; - return anchor; - } - }, - - createNavList: function() { - return $(''); - }, - - createChildNavList: function($parent) { - var $childList = this.createNavList(); - $parent.append($childList); - return $childList; - }, - - generateNavEl: function(anchor, text) { - var $a = $(''); - $a.attr('href', '#' + anchor); - $a.text(text); - var $li = $('
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- $("#search-input").focus(); - } - }); - - $(document).ready(function() { - // do keyword highlighting - /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */ - var mark = function() { - - var referrer = document.URL ; - var paramKey = "q" ; - - if (referrer.indexOf("?") !== -1) { - var qs = referrer.substr(referrer.indexOf('?') + 1); - var qs_noanchor = qs.split('#')[0]; - var qsa = qs_noanchor.split('&'); - var keyword = ""; - - for (var i = 0; i < qsa.length; i++) { - var currentParam = qsa[i].split('='); - - if (currentParam.length !== 2) { - continue; - } - - if (currentParam[0] == paramKey) { - keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20")); - } - } - - if (keyword !== "") { - $(".contents").unmark({ - done: function() { - $(".contents").mark(keyword); - } - }); - } - } - }; - - mark(); - }); -}); - -/* Search term highlighting ------------------------------*/ - -function matchedWords(hit) { - var words = []; - - var hierarchy = hit._highlightResult.hierarchy; - // loop to fetch from lvl0, lvl1, etc. - for (var idx in hierarchy) { - words = words.concat(hierarchy[idx].matchedWords); - } - - var content = hit._highlightResult.content; - if (content) { - words = words.concat(content.matchedWords); - } - - // return unique words - var words_uniq = [...new Set(words)]; - return words_uniq; -} - -function updateHitURL(hit) { - - var words = matchedWords(hit); - var url = ""; - - if (hit.anchor) { - url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor; - } else { - url = hit.url + '?q=' + escape(words.join(" ")); - } - - return url; -} diff --git a/docs/index.html b/docs/index.html deleted file mode 100644 index e8f41e6..0000000 --- a/docs/index.html +++ /dev/null @@ -1,334 +0,0 @@ - - - - - - - -Tidy Anomaly Detection • anomalize - - - - - - - - - - - - -
      -
      - - - - -
      -
      - -
      - -
      -
      -

      anomalize -

      - - - -

      The anomalize package functionality has been superceded by timetk. We suggest you begin to use the timetk::anomalize() to benefit from enhanced functionality to get improvements going forward. Learn more about Anomaly Detection with timetk here.

      -

      The original anomalize package functionality will be maintained for previous code bases that use the legacy functionality.

      -

      To prevent the new timetk functionality from conflicting with old anomalize code, use these lines:

      -
      -library(anomalize)
      -
      -anomalize <- anomalize::anomalize
      -plot_anomalies <- anomalize::plot_anomalies
      - -
      -

      Tidy anomaly detection

      -
      -

      anomalize enables a tidy workflow for detecting anomalies in data. The main functions are time_decompose(), anomalize(), and time_recompose(). When combined, it’s quite simple to decompose time series, detect anomalies, and create bands separating the “normal” data from the anomalous data.

      -
      -

      Anomalize In 2 Minutes (YouTube) -

      -

      Anomalize

      -

      Check out our entire Software Intro Series on YouTube!

      -
      -
      -

      Installation -

      -

      You can install the development version with devtools or the most recent CRAN version with install.packages():

      -
      -# devtools::install_github("business-science/anomalize")
      -install.packages("anomalize")
      -
      -
      -

      How It Works -

      -

      anomalize has three main functions:

      -
        -
      • -time_decompose(): Separates the time series into seasonal, trend, and remainder components
      • -
      • -anomalize(): Applies anomaly detection methods to the remainder component.
      • -
      • -time_recompose(): Calculates limits that separate the “normal” data from the anomalies!
      • -
      -
      -
      -

      Getting Started -

      -

      Load the tidyverse and anomalize packages.

      -
      -library(tidyverse)
      -library(anomalize)
      -
      -# NOTE: timetk now has anomaly detection built in, which 
      -#  will get the new functionality going forward.
      -#  Use this script to prevent overwriting legacy anomalize:
      -
      -anomalize <- anomalize::anomalize
      -plot_anomalies <- anomalize::plot_anomalies
      -

      Next, let’s get some data. anomalize ships with a data set called tidyverse_cran_downloads that contains the daily CRAN download counts for 15 “tidy” packages from 2017-01-01 to 2018-03-01.

      -

      Suppose we want to determine which daily download “counts” are anomalous. It’s as easy as using the three main functions (time_decompose(), anomalize(), and time_recompose()) along with a visualization function, plot_anomalies().

      -
      -library(magrittr)
      -tidyverse_cran_downloads %>%
      -    # Data Manipulation / Anomaly Detection
      -    time_decompose(count, method = "stl") %>%
      -    anomalize(remainder, method = "iqr") %>%
      -    time_recompose() %>%
      -    # Anomaly Visualization
      -    plot_anomalies(time_recomposed = TRUE, ncol = 3, alpha_dots = 0.25) +
      -    ggplot2::labs(title = "Tidyverse Anomalies", subtitle = "STL + IQR Methods") 
      -

      -

      Check out the anomalize Quick Start Guide.

      -
      -
      -

      Reducing Forecast Error by 32% -

      -

      Yes! Anomalize has a new function, clean_anomalies(), that can be used to repair time series prior to forecasting. We have a brand new vignette - Reduce Forecast Error (by 32%) with Cleaned Anomalies.

      -
      -tidyverse_cran_downloads %>%
      -    dplyr::filter(package == "lubridate") %>%
      -    dplyr::ungroup() %>%
      -    time_decompose(count) %>%
      -    anomalize(remainder) %>%
      -  
      -    # New function that cleans & repairs anomalies!
      -    clean_anomalies() %>%
      -  
      -    dplyr::select(date, anomaly, observed, observed_cleaned) %>%
      -    dplyr::filter(anomaly == "Yes")
      -#> # A time tibble: 19 × 4
      -#> # Index:         date
      -#>    date       anomaly  observed observed_cleaned
      -#>    <date>     <chr>       <dbl>            <dbl>
      -#>  1 2017-01-12 Yes     -1.14e-13            3522.
      -#>  2 2017-04-19 Yes      8.55e+ 3            5202.
      -#>  3 2017-09-01 Yes      3.98e-13            4137.
      -#>  4 2017-09-07 Yes      9.49e+ 3            4871.
      -#>  5 2017-10-30 Yes      1.20e+ 4            6413.
      -#>  6 2017-11-13 Yes      1.03e+ 4            6641.
      -#>  7 2017-11-14 Yes      1.15e+ 4            7250.
      -#>  8 2017-12-04 Yes      1.03e+ 4            6519.
      -#>  9 2017-12-05 Yes      1.06e+ 4            7099.
      -#> 10 2017-12-27 Yes      3.69e+ 3            7073.
      -#> 11 2018-01-01 Yes      1.87e+ 3            6418.
      -#> 12 2018-01-05 Yes     -5.68e-14            6293.
      -#> 13 2018-01-13 Yes      7.64e+ 3            4141.
      -#> 14 2018-02-07 Yes      1.19e+ 4            8539.
      -#> 15 2018-02-08 Yes      1.17e+ 4            8237.
      -#> 16 2018-02-09 Yes     -5.68e-14            7780.
      -#> 17 2018-02-10 Yes      0                   5478.
      -#> 18 2018-02-23 Yes     -5.68e-14            8519.
      -#> 19 2018-02-24 Yes      0                   6218.
      -
      -
      -

      But Wait, There’s More! -

      -

      There are a several extra capabilities:

      - -
      -tidyverse_cran_downloads %>%
      -    dplyr::filter(package == "lubridate") %>%
      -    dplyr::ungroup() %>%
      -    time_decompose(count) %>%
      -    anomalize(remainder) %>%
      -    plot_anomaly_decomposition() +
      -    ggplot2::labs(title = "Decomposition of Anomalized Lubridate Downloads")
      -

      -

      For more information on the anomalize methods and the inner workings, please see “Anomalize Methods” Vignette.

      -
      -
      -

      References -

      -

      Several other packages were instrumental in developing anomaly detection methods used in anomalize:

      -
        -
      • Twitter’s AnomalyDetection, which implements decomposition using median spans and the Generalized Extreme Studentized Deviation (GESD) test for anomalies.
      • -
      • -forecast::tsoutliers() function, which implements the IQR method.
      • -
      -
      -
      -
      -

      Interested in Learning Anomaly Detection? -

      -

      Business Science offers two 1-hour courses on Anomaly Detection:

      - -
      - -
      - - -
      - - -
      - -
      -

      -

      Site built with pkgdown 2.0.7.

      -
      - -
      -
      - - - - - - - - diff --git a/docs/jquery.sticky-kit.min.js b/docs/jquery.sticky-kit.min.js deleted file mode 100644 index e2a3c6d..0000000 --- a/docs/jquery.sticky-kit.min.js +++ /dev/null @@ -1,9 +0,0 @@ -/* - Sticky-kit v1.1.2 | WTFPL | Leaf Corcoran 2015 | http://leafo.net -*/ -(function(){var b,f;b=this.jQuery||window.jQuery;f=b(window);b.fn.stick_in_parent=function(d){var A,w,J,n,B,K,p,q,k,E,t;null==d&&(d={});t=d.sticky_class;B=d.inner_scrolling;E=d.recalc_every;k=d.parent;q=d.offset_top;p=d.spacer;w=d.bottoming;null==q&&(q=0);null==k&&(k=void 0);null==B&&(B=!0);null==t&&(t="is_stuck");A=b(document);null==w&&(w=!0);J=function(a,d,n,C,F,u,r,G){var v,H,m,D,I,c,g,x,y,z,h,l;if(!a.data("sticky_kit")){a.data("sticky_kit",!0);I=A.height();g=a.parent();null!=k&&(g=g.closest(k)); -if(!g.length)throw"failed to find stick parent";v=m=!1;(h=null!=p?p&&a.closest(p):b("
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      -
      - - - -
      -
      - - -
      - -

      Prepare for supercession by timetk. Note that anomalize R package will be maintained for backwards compatibility. Users may wish to add these 2 lines of code to existing codebases that use the legacy anomalize R package:

      -
      -
      -library(anomalize)
      -
      -anomalize <- anomalize::anomalize
      -plot_anomalies <- anomalize::plot_anomalies
      -
      -
      - -

      Republish on CRAN.

      -
      -
      - -

      Bug Fixes

      -
      • -theme_tq(): Fix issues with %+replace%, theme_gray, and rel not found.
      • -
      -
      - -

      Bug Fixes

      -
      • Fix issue with sign error in GESD Method (Issue #46).
      • -
      • Require tibbletime >= 0.1.5
      • -
      -
      - -
      • clean_anomalies() - A new function to simplify cleaning anomalies by replacing with trend and seasonal components. This is useful in preparing data for forecasting.

      • -
      • tidyr v1.0.0 and tibbletime v0.1.3 compatability - Improvements to incorporate the upgraded tidyr package.

      • -
      -
      - -
      -
      - -
      • Added a NEWS.md file to track changes to the package.
      • -
      -
      - - - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
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diff --git a/docs/reference/anomalize.html b/docs/reference/anomalize.html deleted file mode 100644 index 5256237..0000000 --- a/docs/reference/anomalize.html +++ /dev/null @@ -1,220 +0,0 @@ - -Detect anomalies using the tidyverse — anomalize • anomalize - - -

      -
      - - - -
      -
      - - -
      -

      The anomalize() function is used to detect outliers in a distribution -with no trend or seasonality present. It takes the output of time_decompose(), -which has be de-trended and applies anomaly detection methods to identify outliers.

      -
      - -
      -
      anomalize(
      -  data,
      -  target,
      -  method = c("iqr", "gesd"),
      -  alpha = 0.05,
      -  max_anoms = 0.2,
      -  verbose = FALSE
      -)
      -
      - -
      -

      Arguments

      -
      data
      -

      A tibble or tbl_time object.

      - - -
      target
      -

      A column to apply the function to

      - - -
      method
      -

      The anomaly detection method. One of "iqr" or "gesd". -The IQR method is faster at the expense of possibly not being quite as accurate. -The GESD method has the best properties for outlier detection, but is loop-based -and therefore a bit slower.

      - - -
      alpha
      -

      Controls the width of the "normal" range. -Lower values are more conservative while higher values are less prone -to incorrectly classifying "normal" observations.

      - - -
      max_anoms
      -

      The maximum percent of anomalies permitted to be identified.

      - - -
      verbose
      -

      A boolean. If TRUE, will return a list containing useful information -about the anomalies. If FALSE, just returns the data expanded with the anomalies and -the lower (l1) and upper (l2) bounds.

      - -
      -
      -

      Value

      - - -

      Returns a tibble / tbl_time object or list depending on the value of verbose.

      -
      -
      -

      Details

      -

      The return has three columns: -"remainder_l1" (lower limit for anomalies), "remainder_l2" (upper limit for -anomalies), and "anomaly" (Yes/No).

      -

      Use time_decompose() to decompose a time series prior to performing -anomaly detection with anomalize(). Typically, anomalize() is -performed on the "remainder" of the time series decomposition.

      -

      For non-time series data (data without trend), the anomalize() function can -be used without time series decomposition.

      -

      The anomalize() function uses two methods for outlier detection -each with benefits.

      -

      IQR:

      -

      The IQR Method uses an innerquartile range of 25% and 75% to establish a baseline distribution around -the median. With the default alpha = 0.05, the limits are established by expanding -the 25/75 baseline by an IQR Factor of 3 (3X). The IQR Factor = 0.15 / alpha (hense 3X with alpha = 0.05). -To increase the IQR Factor controling the limits, decrease the alpha, which makes -it more difficult to be an outlier. Increase alpha to make it easier to be an outlier.

      -

      The IQR method is used in forecast::tsoutliers().

      -

      GESD:

      -

      The GESD Method (Generlized Extreme Studentized Deviate Test) progressively -eliminates outliers using a Student's T-Test comparing the test statistic to a critical value. -Each time an outlier is removed, the test statistic is updated. Once test statistic -drops below the critical value, all outliers are considered removed. Because this method -involves continuous updating via a loop, it is slower than the IQR method. However, it -tends to be the best performing method for outlier removal.

      -

      The GESD method is used in AnomalyDection::AnomalyDetectionTs().

      -
      - -
      -

      See also

      -

      Anomaly Detection Methods (Powers anomalize)

      Time Series Anomaly Detection Functions (anomaly detection workflow):

      -
      - -
      -

      Examples

      -
      if (FALSE) {
      -library(dplyr)
      -
      -# Needed to pass CRAN check / This is loaded by default
      -set_time_scale_template(time_scale_template())
      -
      -data(tidyverse_cran_downloads)
      -
      -tidyverse_cran_downloads %>%
      -    time_decompose(count, method = "stl") %>%
      -    anomalize(remainder, method = "iqr")
      -}
      -
      -
      -
      -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/reference/anomalize_methods.html b/docs/reference/anomalize_methods.html deleted file mode 100644 index 1ccffc4..0000000 --- a/docs/reference/anomalize_methods.html +++ /dev/null @@ -1,294 +0,0 @@ - -Methods that power anomalize() — anomalize_methods • anomalize - - -
      -
      - - - -
      -
      - - -
      -

      Methods that power anomalize()

      -
      - -
      -
      iqr(x, alpha = 0.05, max_anoms = 0.2, verbose = FALSE)
      -
      -gesd(x, alpha = 0.05, max_anoms = 0.2, verbose = FALSE)
      -
      - -
      -

      Arguments

      -
      x
      -

      A vector of numeric data.

      - - -
      alpha
      -

      Controls the width of the "normal" range. -Lower values are more conservative while higher values are less prone -to incorrectly classifying "normal" observations.

      - - -
      max_anoms
      -

      The maximum percent of anomalies permitted to be identified.

      - - -
      verbose
      -

      A boolean. If TRUE, will return a list containing useful information -about the anomalies. If FALSE, just returns a vector of "Yes" / "No" values.

      - -
      -
      -

      Value

      - - -

      Returns character vector or list depending on the value of verbose.

      -
      -
      -

      References

      - -
      -
      -

      See also

      - -
      - -
      -

      Examples

      -
      
      -set.seed(100)
      -x <- rnorm(100)
      -idx_outliers <- sample(100, size = 5)
      -x[idx_outliers] <- x[idx_outliers] + 10
      -
      -iqr(x, alpha = 0.05, max_anoms = 0.2)
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No" "Yes"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No"  "No"  "No" "Yes"  "No"  "No" "Yes"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No" "Yes"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No"  "No"  "No" "Yes"  "No"  "No"  "No" 
      -iqr(x, alpha = 0.05, max_anoms = 0.2, verbose = TRUE)
      -#> $outlier
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No" "Yes"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No"  "No"  "No" "Yes"  "No"  "No" "Yes"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No" "Yes"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>   25%   25%   25%   25%   25%   25%   25%   25%   25% 
      -#>  "No"  "No"  "No"  "No"  "No" "Yes"  "No"  "No"  "No" 
      -#> 
      -#> $outlier_idx
      -#> [1] 74 71 30 82 97
      -#> 
      -#> $outlier_vals
      -#> [1] 11.648522 10.448903 10.247076  9.950004  9.167504
      -#> 
      -#> $outlier_direction
      -#> [1] "Up" "Up" "Up" "Up" "Up"
      -#> 
      -#> $critical_limits
      -#> limit_lower.25% limit_upper.75% 
      -#>       -4.552347        4.755455 
      -#> 
      -#> $outlier_report
      -#> # A tibble: 20 × 7
      -#>     rank index value limit_lower limit_upper outlier direction
      -#>    <dbl> <dbl> <dbl>       <dbl>       <dbl> <chr>   <chr>    
      -#>  1     1    74 11.6        -4.55        4.76 Yes     Up       
      -#>  2     2    71 10.4        -4.55        4.76 Yes     Up       
      -#>  3     3    30 10.2        -4.55        4.76 Yes     Up       
      -#>  4     4    82  9.95       -4.55        4.76 Yes     Up       
      -#>  5     5    97  9.17       -4.55        4.76 Yes     Up       
      -#>  6     6    64  2.58       -4.55        4.76 No      NA       
      -#>  7     7    55 -2.27       -4.55        4.76 No      NA       
      -#>  8     8    96  2.45       -4.55        4.76 No      NA       
      -#>  9     9    20  2.31       -4.55        4.76 No      NA       
      -#> 10    10    80 -2.07       -4.55        4.76 No      NA       
      -#> 11    11    75 -2.06       -4.55        4.76 No      NA       
      -#> 12    12    84 -1.93       -4.55        4.76 No      NA       
      -#> 13    13    50 -1.88       -4.55        4.76 No      NA       
      -#> 14    14    43 -1.78       -4.55        4.76 No      NA       
      -#> 15    15    52 -1.74       -4.55        4.76 No      NA       
      -#> 16    16    54  1.90       -4.55        4.76 No      NA       
      -#> 17    17    58  1.82       -4.55        4.76 No      NA       
      -#> 18    18    32  1.76       -4.55        4.76 No      NA       
      -#> 19    19    89  1.73       -4.55        4.76 No      NA       
      -#> 20    20    57 -1.40       -4.55        4.76 No      NA       
      -#> 
      -
      -gesd(x, alpha = 0.05, max_anoms = 0.2)
      -#>   [1] "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>  [13] "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>  [25] "No"  "No"  "No"  "No"  "No"  "Yes" "No"  "No"  "No"  "No"  "No"  "No" 
      -#>  [37] "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>  [49] "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>  [61] "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "Yes" "No" 
      -#>  [73] "No"  "Yes" "No"  "No"  "No"  "No"  "No"  "No"  "No"  "Yes" "No"  "No" 
      -#>  [85] "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>  [97] "Yes" "No"  "No"  "No" 
      -gesd(x, alpha = 0.05, max_anoms = 0.2, verbose = TRUE)
      -#> $outlier
      -#>   [1] "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>  [13] "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>  [25] "No"  "No"  "No"  "No"  "No"  "Yes" "No"  "No"  "No"  "No"  "No"  "No" 
      -#>  [37] "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>  [49] "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>  [61] "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "Yes" "No" 
      -#>  [73] "No"  "Yes" "No"  "No"  "No"  "No"  "No"  "No"  "No"  "Yes" "No"  "No" 
      -#>  [85] "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No"  "No" 
      -#>  [97] "Yes" "No"  "No"  "No" 
      -#> 
      -#> $outlier_idx
      -#> [1] 74 71 30 82 97
      -#> 
      -#> $outlier_vals
      -#> [1] 11.648522 10.448903 10.247076  9.950004  9.167504
      -#> 
      -#> $outlier_direction
      -#> [1] "Up" "Up" "Up" "Up" "Up"
      -#> 
      -#> $critical_limits
      -#> limit_lower limit_upper 
      -#>   -3.315690    3.175856 
      -#> 
      -#> $outlier_report
      -#> # A tibble: 20 × 7
      -#>     rank index value limit_lower limit_upper outlier direction
      -#>    <dbl> <dbl> <dbl>       <dbl>       <dbl> <chr>   <chr>    
      -#>  1     1    74 11.6        -3.60        3.58 Yes     Up       
      -#>  2     2    71 10.4        -3.49        3.43 Yes     Up       
      -#>  3     3    30 10.2        -3.45        3.35 Yes     Up       
      -#>  4     4    82  9.95       -3.53        3.39 Yes     Up       
      -#>  5     5    97  9.17       -3.42        3.29 Yes     Up       
      -#>  6     6    64  2.58       -3.32        3.18 No      NA       
      -#>  7     7    96  2.45       -3.28        3.13 No      NA       
      -#>  8     8    20  2.31       -3.24        3.08 No      NA       
      -#>  9     9    55 -2.27       -3.15        2.98 No      NA       
      -#> 10    10    80 -2.07       -3.12        2.96 No      NA       
      -#> 11    11    75 -2.06       -3.05        2.91 No      NA       
      -#> 12    12    54  1.90       -2.95        2.81 No      NA       
      -#> 13    13    58  1.82       -2.78        2.63 No      NA       
      -#> 14    14    84 -1.93       -2.57        2.41 No      NA       
      -#> 15    15    32  1.76       -2.54        2.39 No      NA       
      -#> 16    16    89  1.73       -2.53        2.37 No      NA       
      -#> 17    17    50 -1.88       -2.54        2.37 No      NA       
      -#> 18    18    43 -1.78       -2.50        2.34 No      NA       
      -#> 19    19    52 -1.74       -2.46        2.31 No      NA       
      -#> 20    20    92  1.43       -2.44        2.30 No      NA       
      -#> 
      -
      -
      -
      -
      -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/reference/anomalize_package.html b/docs/reference/anomalize_package.html deleted file mode 100644 index 6e32cfb..0000000 --- a/docs/reference/anomalize_package.html +++ /dev/null @@ -1,130 +0,0 @@ - -anomalize: Tidy anomaly detection — anomalize_package • anomalize - - -
      -
      - - - -
      -
      - - -
      -

      The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data. -The main functions are time_decompose(), anomalize(), and time_recompose(). -When combined, it's quite simple to decompose time series, detect anomalies, -and create bands separating the "normal" data from the anomalous data at scale (i.e. for multiple time series). -Time series decomposition is used to remove trend and seasonal components via the time_decompose() function -and methods include seasonal decomposition of time series by Loess and -seasonal decomposition by piecewise medians. The anomalize() function implements -two methods for anomaly detection of residuals including using an inner quartile range -and generalized extreme studentized deviation. These methods are based on -those used in the forecast package and the Twitter AnomalyDetection package. -Refer to the associated functions for specific references for these methods.

      -
      - - -
      -

      Details

      -

      To learn more about anomalize, start with the vignettes: -browseVignettes(package = "anomalize")

      -
      - -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/reference/clean_anomalies.html b/docs/reference/clean_anomalies.html deleted file mode 100644 index 7163ebe..0000000 --- a/docs/reference/clean_anomalies.html +++ /dev/null @@ -1,153 +0,0 @@ - -Clean anomalies from anomalized data — clean_anomalies • anomalize - - -
      -
      - - - -
      -
      - - -
      -

      Clean anomalies from anomalized data

      -
      - -
      -
      clean_anomalies(data)
      -
      - -
      -

      Arguments

      -
      data
      -

      A tibble or tbl_time object.

      - -
      -
      -

      Value

      - - -

      Returns a tibble / tbl_time object with a new column "observed_cleaned".

      -
      -
      -

      Details

      -

      The clean_anomalies() function is used to replace outliers with the seasonal and trend component. -This is often desirable when forecasting with noisy time series data to improve trend detection.

      -

      To clean anomalies, the input data must be detrended with time_decompose() and anomalized with anomalize(). -The data can also be recomposed with time_recompose().

      -
      -
      -

      See also

      -

      Time Series Anomaly Detection Functions (anomaly detection workflow):

      -
      - -
      -

      Examples

      -
      
      -if (FALSE) {
      -library(dplyr)
      -
      -# Needed to pass CRAN check / This is loaded by default
      -set_time_scale_template(time_scale_template())
      -
      -data(tidyverse_cran_downloads)
      -
      -tidyverse_cran_downloads %>%
      -    time_decompose(count, method = "stl") %>%
      -    anomalize(remainder, method = "iqr") %>%
      -    clean_anomalies()
      -}
      -
      -
      -
      -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/reference/decompose_methods.html b/docs/reference/decompose_methods.html deleted file mode 100644 index 7312324..0000000 --- a/docs/reference/decompose_methods.html +++ /dev/null @@ -1,198 +0,0 @@ - -Methods that power time_decompose() — decompose_methods • anomalize - - -
      -
      - - - -
      -
      - - -
      -

      Methods that power time_decompose()

      -
      - -
      -
      decompose_twitter(
      -  data,
      -  target,
      -  frequency = "auto",
      -  trend = "auto",
      -  message = TRUE
      -)
      -
      -decompose_stl(data, target, frequency = "auto", trend = "auto", message = TRUE)
      -
      - -
      -

      Arguments

      -
      data
      -

      A tibble or tbl_time object.

      - - -
      target
      -

      A column to apply the function to

      - - -
      frequency
      -

      Controls the seasonal adjustment (removal of seasonality). -Input can be either "auto", a time-based definition (e.g. "1 week"), -or a numeric number of observations per frequency (e.g. 10). -Refer to time_frequency().

      - - -
      trend
      -

      Controls the trend component -For stl, the trend controls the sensitivity of the lowess smoother, which is used to remove the remainder. -For twitter, the trend controls the period width of the median, which are used to remove the trend and center the remainder.

      - - -
      message
      -

      A boolean. If TRUE, will output information related to tbl_time conversions, frequencies, -and trend / median spans (if applicable).

      - -
      -
      -

      Value

      - - -

      A tbl_time object containing the time series decomposition.

      -
      -
      -

      References

      - -
      -
      -

      See also

      - -
      - -
      -

      Examples

      -
      
      -library(dplyr)
      -#> Warning: package 'dplyr' was built under R version 4.3.2
      -#> 
      -#> Attaching package: 'dplyr'
      -#> The following objects are masked from 'package:stats':
      -#> 
      -#>     filter, lag
      -#> The following objects are masked from 'package:base':
      -#> 
      -#>     intersect, setdiff, setequal, union
      -
      -tidyverse_cran_downloads %>%
      -    ungroup() %>%
      -    filter(package == "tidyquant") %>%
      -    decompose_stl(count)
      -#> frequency = 7 days
      -#> trend = 91 days
      -#> # A time tibble: 425 × 5
      -#> # Index:         date
      -#>    date       observed season trend remainder
      -#>    <date>        <dbl>  <dbl> <dbl>     <dbl>
      -#>  1 2017-01-01        9 -19.8   27.3     1.46 
      -#>  2 2017-01-02       55  12.4   27.4    15.2  
      -#>  3 2017-01-03       48  11.3   27.4     9.28 
      -#>  4 2017-01-04       25   8.91  27.4   -11.4  
      -#>  5 2017-01-05       22   9.80  27.5   -15.3  
      -#>  6 2017-01-06        7  -1.26  27.5   -19.3  
      -#>  7 2017-01-07        7 -21.3   27.5     0.807
      -#>  8 2017-01-08       32 -19.8   27.6    24.2  
      -#>  9 2017-01-09       70  12.4   27.6    30.0  
      -#> 10 2017-01-10       33  11.3   27.6    -5.95 
      -#> # ℹ 415 more rows
      -
      -
      -
      -
      -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
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ztUrM%*I=c5c`2_<%H#wgd#&BQsw;W5sLq;;D-=C8u^!eQ-rs^qr)3wTH3%Lrm}Kro zxb@X7zwWg_;P&11@lg*~E6u&rlUDLKbavLb{|_fnbbA|s`EgY)Pphb`-jSa75EiRk z2LX~1?;jqrTs8@g45nL^q|7FC(0?SZ;F3Fg^U1i9{q_H92!yy)-YYVkx5 z&u%=+zmdi3>Y?ihGCmHwnpUjfYh&Zf`TEUcgBEA*S4es0#>F*%A;@aXPTafi=77a! zp#vq$P8WF@teI1;bqCUl|3sImtNM9udd?nlBQe?VPJ zYCLp6`8v~tW>&~LoyX>o`VKnwa=nOu?xETE91kK*nxOsYefqRDWq6HSxcWP1jmFOF zgOn+nnMa9iv6W&(LamZAf^?&eS@`l!|K*^V8Me*6Zh7-y -Function reference • anomalize - - -

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      General

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      anomalize_package anomalize-package

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      anomalize: Tidy anomaly detection

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      tidyverse_cran_downloads

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      Downloads of various "tidyverse" packages from CRAN

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      Anomalize workflow

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      The main functions used to anomalize time series data.

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      time_decompose()

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      Decompose a time series in preparation for anomaly detection

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      anomalize()

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      Detect anomalies using the tidyverse

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      time_recompose()

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      Recompose bands separating anomalies from "normal" observations

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      clean_anomalies()

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      Clean anomalies from anomalized data

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      Visualization functions

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      Plotting utilities for visualizing anomalies.

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      plot_anomalies()

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      Visualize the anomalies in one or multiple time series

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      plot_anomaly_decomposition()

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      Visualize the time series decomposition with anomalies shown

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      Frequency and trend

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      Working with the frequency, trend, and time scale.

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      time_frequency() time_trend()

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      Generate a time series frequency from a periodicity

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      set_time_scale_template() get_time_scale_template() time_scale_template()

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      Get and modify time scale template

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      Methods

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      Functions that power the main anomalize functions.

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      decompose_twitter() decompose_stl()

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      Methods that power time_decompose()

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      iqr() gesd()

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      Methods that power anomalize()

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      Misc

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      Miscellaneous functions and utilites.

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      prep_tbl_time()

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      Automatically create tibbletime objects from tibbles

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      time_apply()

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      Apply a function to a time series by period

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      Site built with pkgdown 2.0.7.

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zq$I}UwB6H63gRI(89Bb?wq}`F0W<-278K0-K#WEosw{*vJGui9<-uz{aGxosvP -Visualize the time series decomposition with anomalies shown — plot_anomaly_decomposition • anomalize - - -

      -
      - - - -
      -
      - - -
      -

      Visualize the time series decomposition with anomalies shown

      -
      - -
      -
      plot_anomaly_decomposition(
      -  data,
      -  ncol = 1,
      -  color_no = "#2c3e50",
      -  color_yes = "#e31a1c",
      -  alpha_dots = 1,
      -  alpha_circles = 1,
      -  size_dots = 1.5,
      -  size_circles = 4,
      -  strip.position = "right"
      -)
      -
      - -
      -

      Arguments

      -
      data
      -

      A tibble or tbl_time object.

      - - -
      ncol
      -

      Number of columns to display. Set to 1 for single column by default.

      - - -
      color_no
      -

      Color for non-anomalous data.

      - - -
      color_yes
      -

      Color for anomalous data.

      - - -
      alpha_dots
      -

      Controls the transparency of the dots. Reduce when too many dots on the screen.

      - - -
      alpha_circles
      -

      Controls the transparency of the circles that identify anomalies.

      - - -
      size_dots
      -

      Controls the size of the dots.

      - - -
      size_circles
      -

      Controls the size of the circles that identify anomalies.

      - - -
      strip.position
      -

      Controls the placement of the strip that identifies the time series decomposition components.

      - -
      -
      -

      Value

      - - -

      Returns a ggplot object.

      -
      -
      -

      Details

      -

      The first step in reviewing the anomaly detection process is to evaluate -a single times series to observe how the algorithm is selecting anomalies. -The plot_anomaly_decomposition() function is used to gain -an understanding as to whether or not the method is detecting anomalies correctly and -whether or not parameters such as decomposition method, anomalize method, -alpha, frequency, and so on should be adjusted.

      -
      -
      -

      See also

      - -
      - -
      -

      Examples

      -
      
      -library(dplyr)
      -library(ggplot2)
      -
      -data(tidyverse_cran_downloads)
      -
      -tidyverse_cran_downloads %>%
      -    filter(package == "tidyquant") %>%
      -    ungroup() %>%
      -    time_decompose(count, method = "stl") %>%
      -    anomalize(remainder, method = "iqr") %>%
      -    plot_anomaly_decomposition()
      -#> frequency = 7 days
      -#> trend = 91 days
      -
      -
      -
      -
      -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/reference/prep_tbl_time.html b/docs/reference/prep_tbl_time.html deleted file mode 100644 index 2ef2b87..0000000 --- a/docs/reference/prep_tbl_time.html +++ /dev/null @@ -1,164 +0,0 @@ - -Automatically create tibbletime objects from tibbles — prep_tbl_time • anomalize - - -
      -
      - - - -
      -
      - - -
      -

      Automatically create tibbletime objects from tibbles

      -
      - -
      -
      prep_tbl_time(data, message = FALSE)
      -
      - -
      -

      Arguments

      -
      data
      -

      A tibble.

      - - -
      message
      -

      A boolean. If TRUE, returns a message indicating any -conversion details important to know during the conversion to tbl_time class.

      - -
      -
      -

      Value

      - - -

      Returns a tibbletime object of class tbl_time.

      -
      -
      -

      Details

      -

      Detects a date or datetime index column and automatically

      -
      - -
      -

      Examples

      -
      
      -library(dplyr)
      -library(tibbletime)
      -#> Warning: package 'tibbletime' was built under R version 4.3.2
      -#> 
      -#> Attaching package: 'tibbletime'
      -#> The following object is masked from 'package:stats':
      -#> 
      -#>     filter
      -
      -data_tbl <- tibble(
      -    date  = seq.Date(from = as.Date("2018-01-01"), by = "day", length.out = 10),
      -    value = rnorm(10)
      -    )
      -
      -prep_tbl_time(data_tbl)
      -#> # A time tibble: 10 × 2
      -#> # Index:         date
      -#>    date        value
      -#>    <date>      <dbl>
      -#>  1 2018-01-01  1.16 
      -#>  2 2018-01-02  0.283
      -#>  3 2018-01-03 -0.198
      -#>  4 2018-01-04  0.680
      -#>  5 2018-01-05 -0.547
      -#>  6 2018-01-06  0.337
      -#>  7 2018-01-07  0.656
      -#>  8 2018-01-08 -1.80 
      -#>  9 2018-01-09 -0.153
      -#> 10 2018-01-10  1.66 
      -
      -
      -
      -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/reference/tidyverse_cran_downloads.html b/docs/reference/tidyverse_cran_downloads.html deleted file mode 100644 index 994f64c..0000000 --- a/docs/reference/tidyverse_cran_downloads.html +++ /dev/null @@ -1,156 +0,0 @@ - -Downloads of various "tidyverse" packages from CRAN — tidyverse_cran_downloads • anomalize - - -
      -
      - - - -
      -
      - - -
      -

      A dataset containing the daily download counts from 2017-01-01 to 2018-03-01 -for the following tidyverse packages:

      • tidyr

      • -
      • lubridate

      • -
      • dplyr

      • -
      • broom

      • -
      • tidyquant

      • -
      • tidytext

      • -
      • ggplot2

      • -
      • purrr

      • -
      • stringr

      • -
      • forcats

      • -
      • knitr

      • -
      • readr

      • -
      • tibble

      • -
      • tidyverse

      • -
      - -
      -
      tidyverse_cran_downloads
      -
      - -
      -

      Format

      -

      A grouped_tbl_time object with 6,375 rows and 3 variables:

      date
      -

      Date of the daily observation

      - -
      count
      -

      Number of downloads that day

      - -
      package
      -

      The package corresponding to the daily download number

      - - -
      -
      -

      Source

      -

      The package downloads come from CRAN by way of the cranlogs package.

      -
      - -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/reference/time_apply.html b/docs/reference/time_apply.html deleted file mode 100644 index fe12e46..0000000 --- a/docs/reference/time_apply.html +++ /dev/null @@ -1,208 +0,0 @@ - -Apply a function to a time series by period — time_apply • anomalize - - -
      -
      - - - -
      -
      - - -
      -

      Apply a function to a time series by period

      -
      - -
      -
      time_apply(
      -  data,
      -  target,
      -  period,
      -  .fun,
      -  ...,
      -  start_date = NULL,
      -  side = "end",
      -  clean = FALSE,
      -  message = TRUE
      -)
      -
      - -
      -

      Arguments

      -
      data
      -

      A tibble with a date or datetime index.

      - - -
      target
      -

      A column to apply the function to

      - - -
      period
      -

      A time-based definition (e.g. "1 week"). -or a numeric number of observations per frequency (e.g. 10). -See tibbletime::collapse_by() for period notation.

      - - -
      .fun
      -

      A function to apply (e.g. median)

      - - -
      ...
      -

      Additional parameters passed to the function, .fun

      - - -
      start_date
      -

      Optional argument used to -specify the start date for the -first group. The default is to start at the closest period boundary -below the minimum date in the supplied index.

      - - -
      side
      -

      Whether to return the date at the beginning or the end of -the new period. By default, the "end" of the period. -Use "start" to change to the start of the period.

      - - -
      clean
      -

      Whether or not to round the collapsed index up / down to the next -period boundary. The decision to round up / down is controlled by the side -argument.

      - - -
      message
      -

      A boolean. If message = TRUE, the frequency used is output -along with the units in the scale of the data.

      - -
      -
      -

      Value

      - - -

      Returns a tibbletime object of class tbl_time.

      -
      -
      -

      Details

      -

      Uses a time-based period to apply functions to. This is useful in circumstances where you want to -compare the observation values to aggregated values such as mean() or median() -during a set time-based period. The returned output extends the -length of the data frame so the differences can easily be computed.

      -
      - -
      -

      Examples

      -
      
      -library(dplyr)
      -
      -data(tidyverse_cran_downloads)
      -
      -# Basic Usage
      -tidyverse_cran_downloads %>%
      -    time_apply(count, period = "1 week", .fun = mean, na.rm = TRUE)
      -#> # A time tibble: 6,375 × 4
      -#> # Index:         date
      -#> # Groups:        package [15]
      -#>    package date       count time_apply
      -#>    <chr>   <date>     <dbl>      <dbl>
      -#>  1 broom   2017-01-01  1053      1678.
      -#>  2 broom   2017-01-02  1481      1678.
      -#>  3 broom   2017-01-03  1851      1678.
      -#>  4 broom   2017-01-04  1947      1678.
      -#>  5 broom   2017-01-05  1927      1678.
      -#>  6 broom   2017-01-06  1948      1678.
      -#>  7 broom   2017-01-07  1542      1678.
      -#>  8 broom   2017-01-08  1479      1716 
      -#>  9 broom   2017-01-09  2057      1716 
      -#> 10 broom   2017-01-10  2278      1716 
      -#> # ℹ 6,365 more rows
      -
      -
      -
      -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/reference/time_decompose.html b/docs/reference/time_decompose.html deleted file mode 100644 index a204fc8..0000000 --- a/docs/reference/time_decompose.html +++ /dev/null @@ -1,272 +0,0 @@ - -Decompose a time series in preparation for anomaly detection — time_decompose • anomalize - - -
      -
      - - - -
      -
      - - -
      -

      Decompose a time series in preparation for anomaly detection

      -
      - -
      -
      time_decompose(
      -  data,
      -  target,
      -  method = c("stl", "twitter"),
      -  frequency = "auto",
      -  trend = "auto",
      -  ...,
      -  merge = FALSE,
      -  message = TRUE
      -)
      -
      - -
      -

      Arguments

      -
      data
      -

      A tibble or tbl_time object.

      - - -
      target
      -

      A column to apply the function to

      - - -
      method
      -

      The time series decomposition method. One of "stl" or "twitter". -The STL method uses seasonal decomposition (see decompose_stl()). -The Twitter method uses trend to remove the trend (see decompose_twitter()).

      - - -
      frequency
      -

      Controls the seasonal adjustment (removal of seasonality). -Input can be either "auto", a time-based definition (e.g. "1 week"), -or a numeric number of observations per frequency (e.g. 10). -Refer to time_frequency().

      - - -
      trend
      -

      Controls the trend component -For stl, the trend controls the sensitivity of the lowess smoother, which is used to remove the remainder. -For twitter, the trend controls the period width of the median, which are used to remove the trend and center the remainder.

      - - -
      ...
      -

      Additional parameters passed to the underlying method functions.

      - - -
      merge
      -

      A boolean. FALSE by default. If TRUE, will append results to the original data.

      - - -
      message
      -

      A boolean. If TRUE, will output information related to tbl_time conversions, frequencies, -and trend / median spans (if applicable).

      - -
      -
      -

      Value

      - - -

      Returns a tbl_time object.

      -
      -
      -

      Details

      -

      The time_decompose() function generates a time series decomposition on -tbl_time objects. The function is "tidy" in the sense that it works -on data frames. It is designed to work with time-based data, and as such -must have a column that contains date or datetime information. The function -also works with grouped data. The function implements several methods -of time series decomposition, each with benefits.

      -

      STL:

      -

      The STL method (method = "stl") implements time series decomposition using -the underlying decompose_stl() function. If you are familiar with stats::stl(), -the function is a "tidy" version that is designed to work with tbl_time objects. -The decomposition separates the "season" and "trend" components from -the "observed" values leaving the "remainder" for anomaly detection. -The user can control two parameters: frequency and trend. -The frequency parameter adjusts the "season" component that is removed -from the "observed" values. The trend parameter adjusts the -trend window (t.window parameter from stl()) that is used. -The user may supply both frequency -and trend as time-based durations (e.g. "90 days") or numeric values -(e.g. 180) or "auto", which predetermines the frequency and/or trend -based on the scale of the time series.

      -

      Twitter:

      -

      The Twitter method (method = "twitter") implements time series decomposition using -the methodology from the Twitter AnomalyDetection package. -The decomposition separates the "seasonal" component and then removes -the median data, which is a different approach than the STL method for removing -the trend. This approach works very well for low-growth + high seasonality data. -STL may be a better approach when trend is a large factor. -The user can control two parameters: frequency and trend. -The frequency parameter adjusts the "season" component that is removed -from the "observed" values. The trend parameter adjusts the -period width of the median spans that are used. The user may supply both frequency -and trend as time-based durations (e.g. "90 days") or numeric values -(e.g. 180) or "auto", which predetermines the frequency and/or median spans -based on the scale of the time series.

      -
      -
      -

      References

      - -
      1. CLEVELAND, R. B., CLEVELAND, W. S., MCRAE, J. E., AND TERPENNING, I. -STL: A Seasonal-Trend Decomposition Procedure Based on Loess. Journal of Official Statistics, Vol. 6, No. 1 (1990), pp. 3-73.

      2. -
      3. Owen S. Vallis, Jordan Hochenbaum and Arun Kejariwal (2014). A Novel Technique for Long-Term Anomaly Detection in the Cloud. Twitter Inc.

      4. -
      5. Owen S. Vallis, Jordan Hochenbaum and Arun Kejariwal (2014). AnomalyDetection: Anomaly Detection Using Seasonal Hybrid Extreme Studentized Deviate Test. R package version 1.0.

      6. -
      -
      -

      See also

      -

      Decomposition Methods (Powers time_decompose)

      Time Series Anomaly Detection Functions (anomaly detection workflow):

      -
      - -
      -

      Examples

      -
      
      -library(dplyr)
      -
      -data(tidyverse_cran_downloads)
      -
      -# Basic Usage
      -tidyverse_cran_downloads %>%
      -    time_decompose(count, method = "stl")
      -#> # A time tibble: 6,375 × 6
      -#> # Index:         date
      -#> # Groups:        package [15]
      -#>    package date       observed season trend remainder
      -#>    <chr>   <date>        <dbl>  <dbl> <dbl>     <dbl>
      -#>  1 broom   2017-01-01     1053 -1007. 1708.    352.  
      -#>  2 broom   2017-01-02     1481   340. 1731.   -589.  
      -#>  3 broom   2017-01-03     1851   563. 1753.   -465.  
      -#>  4 broom   2017-01-04     1947   526. 1775.   -354.  
      -#>  5 broom   2017-01-05     1927   430. 1798.   -301.  
      -#>  6 broom   2017-01-06     1948   136. 1820.     -8.11
      -#>  7 broom   2017-01-07     1542  -988. 1842.    688.  
      -#>  8 broom   2017-01-08     1479 -1007. 1864.    622.  
      -#>  9 broom   2017-01-09     2057   340. 1887.   -169.  
      -#> 10 broom   2017-01-10     2278   563. 1909.   -194.  
      -#> # ℹ 6,365 more rows
      -
      -# twitter
      -tidyverse_cran_downloads %>%
      -    time_decompose(count,
      -                   method       = "twitter",
      -                   frequency    = "1 week",
      -                   trend        = "2 months",
      -                   merge        = TRUE,
      -                   message      = FALSE)
      -#> # A time tibble: 6,375 × 7
      -#> # Index:         date
      -#> # Groups:        package [15]
      -#>    package date       count observed season median_spans remainder
      -#>    <chr>   <date>     <dbl>    <dbl>  <dbl>        <dbl>     <dbl>
      -#>  1 broom   2017-01-01  1053     1053 -871.          2337    -413. 
      -#>  2 broom   2017-01-02  1481     1481  304.          2337   -1160. 
      -#>  3 broom   2017-01-03  1851     1851  503.          2337    -989. 
      -#>  4 broom   2017-01-04  1947     1947  485.          2337    -875. 
      -#>  5 broom   2017-01-05  1927     1927  394.          2337    -804. 
      -#>  6 broom   2017-01-06  1948     1948   54.8         2337    -444. 
      -#>  7 broom   2017-01-07  1542     1542 -870.          2337      74.7
      -#>  8 broom   2017-01-08  1479     1479 -871.          2337      13.1
      -#>  9 broom   2017-01-09  2057     2057  304.          2337    -584. 
      -#> 10 broom   2017-01-10  2278     2278  503.          2337    -562. 
      -#> # ℹ 6,365 more rows
      -
      -
      -
      -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/reference/time_frequency.html b/docs/reference/time_frequency.html deleted file mode 100644 index 69c9ba2..0000000 --- a/docs/reference/time_frequency.html +++ /dev/null @@ -1,215 +0,0 @@ - -Generate a time series frequency from a periodicity — time_frequency • anomalize - - -
      -
      - - - -
      -
      - - -
      -

      Generate a time series frequency from a periodicity

      -
      - -
      -
      time_frequency(data, period = "auto", message = TRUE)
      -
      -time_trend(data, period = "auto", message = TRUE)
      -
      - -
      -

      Arguments

      -
      data
      -

      A tibble with a date or datetime index.

      - - -
      period
      -

      Either "auto", a time-based definition (e.g. "14 days"), -or a numeric number of observations per frequency (e.g. 10). -See tibbletime::collapse_by() for period notation.

      - - -
      message
      -

      A boolean. If message = TRUE, the frequency used is output -along with the units in the scale of the data.

      - -
      -
      -

      Value

      - - -

      Returns a scalar numeric value indicating the number of observations in the frequency or trend span.

      -
      -
      -

      Details

      -

      A frequency is loosely defined as the number of observations that comprise a cycle -in a data set. The trend is loosely defined as time span that can -be aggregated across to visualize the central tendency of the data. -It's often easiest to think of frequency and trend in terms of the time-based units -that the data is already in. This is what time_frequency() and time_trend() -enable: using time-based periods to define the frequency or trend.

      -

      Frequency:

      -

      As an example, a weekly cycle is often 5-days (for working -days) or 7-days (for calendar days). Rather than specify a frequency of 5 or 7, -the user can specify period = "1 week", and -time_frequency()` will detect the scale of the time series and return 5 or 7 -based on the actual data.

      -

      The period argument has three basic options for returning a frequency. -Options include:

      • "auto": A target frequency is determined using a pre-defined template (see template below).

      • -
      • time-based duration: (e.g. "1 week" or "2 quarters" per cycle)

      • -
      • numeric number of observations: (e.g. 5 for 5 observations per cycle)

      • -

      The template argument is only used when period = "auto". The template is a tibble -of three features: time_scale, frequency, and trend. The algorithm will inspect -the scale of the time series and select the best frequency that matches the scale and -number of observations per target frequency. A frequency is then chosen on be the -best match. The predefined template is stored in a function time_scale_template(). -However, the user can come up with his or her own template changing the values -for frequency in the data frame and saving it to anomalize_options$time_scale_template.

      -

      Trend:

      -

      As an example, the trend of daily data is often best aggregated by evaluating -the moving average over a quarter or a month span. Rather than specify the number -of days in a quarter or month, the user can specify "1 quarter" or "1 month", -and the time_trend() function will return the correct number of observations -per trend cycle. In addition, there is an option, period = "auto", to -auto-detect an appropriate trend span depending on the data. The template -is used to define the appropriate trend span.

      -
      - -
      -

      Examples

      -
      
      -library(dplyr)
      -
      -data(tidyverse_cran_downloads)
      -
      -#### FREQUENCY DETECTION ####
      -
      -# period = "auto"
      -tidyverse_cran_downloads %>%
      -    filter(package == "tidyquant") %>%
      -    ungroup() %>%
      -    time_frequency(period = "auto")
      -#> frequency = 7 days
      -#> [1] 7
      -
      -time_scale_template()
      -#> # A tibble: 8 × 3
      -#>   time_scale frequency trend   
      -#>   <chr>      <chr>     <chr>   
      -#> 1 second     1 hour    12 hours
      -#> 2 minute     1 day     14 days 
      -#> 3 hour       1 day     1 month 
      -#> 4 day        1 week    3 months
      -#> 5 week       1 quarter 1 year  
      -#> 6 month      1 year    5 years 
      -#> 7 quarter    1 year    10 years
      -#> 8 year       5 years   30 years
      -
      -# period = "1 month"
      -tidyverse_cran_downloads %>%
      -    filter(package == "tidyquant") %>%
      -    ungroup() %>%
      -    time_frequency(period = "1 month")
      -#> frequency = 31 days
      -#> [1] 31
      -
      -#### TREND DETECTION ####
      -
      -tidyverse_cran_downloads %>%
      -    filter(package == "tidyquant") %>%
      -    ungroup() %>%
      -    time_trend(period = "auto")
      -#> trend = 91 days
      -#> [1] 91
      -
      -
      -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/reference/time_recompose.html b/docs/reference/time_recompose.html deleted file mode 100644 index a6c7811..0000000 --- a/docs/reference/time_recompose.html +++ /dev/null @@ -1,171 +0,0 @@ - -Recompose bands separating anomalies from "normal" observations — time_recompose • anomalize - - -
      -
      - - - -
      -
      - - -
      -

      Recompose bands separating anomalies from "normal" observations

      -
      - -
      -
      time_recompose(data)
      -
      - -
      -

      Arguments

      -
      data
      -

      A tibble or tbl_time object that has been -processed with time_decompose() and anomalize().

      - -
      -
      -

      Value

      - - -

      Returns a tbl_time object.

      -
      -
      -

      Details

      -

      The time_recompose() function is used to generate bands around the -"normal" levels of observed values. The function uses the remainder_l1 -and remainder_l2 levels produced during the anomalize() step -and the season and trend/median_spans values from the time_decompose() -step to reconstruct bands around the normal values.

      -

      The following key names are required: observed:remainder from the -time_decompose() step and remainder_l1 and remainder_l2 from the -anomalize() step.

      -
      -
      -

      See also

      -

      Time Series Anomaly Detection Functions (anomaly detection workflow):

      -
      - -
      -

      Examples

      -
      
      -library(dplyr)
      -
      -data(tidyverse_cran_downloads)
      -
      -# Basic Usage
      -tidyverse_cran_downloads %>%
      -    time_decompose(count, method = "stl") %>%
      -    anomalize(remainder, method = "iqr") %>%
      -    time_recompose()
      -#> # A time tibble: 6,375 × 11
      -#> # Index:         date
      -#> # Groups:        package [15]
      -#>    package date       observed season trend remainder remainder_l1 remainder_l2
      -#>    <chr>   <date>        <dbl>  <dbl> <dbl>     <dbl>        <dbl>        <dbl>
      -#>  1 broom   2017-01-01     1053 -1007. 1708.    352.         -1725.        1704.
      -#>  2 broom   2017-01-02     1481   340. 1731.   -589.         -1725.        1704.
      -#>  3 broom   2017-01-03     1851   563. 1753.   -465.         -1725.        1704.
      -#>  4 broom   2017-01-04     1947   526. 1775.   -354.         -1725.        1704.
      -#>  5 broom   2017-01-05     1927   430. 1798.   -301.         -1725.        1704.
      -#>  6 broom   2017-01-06     1948   136. 1820.     -8.11       -1725.        1704.
      -#>  7 broom   2017-01-07     1542  -988. 1842.    688.         -1725.        1704.
      -#>  8 broom   2017-01-08     1479 -1007. 1864.    622.         -1725.        1704.
      -#>  9 broom   2017-01-09     2057   340. 1887.   -169.         -1725.        1704.
      -#> 10 broom   2017-01-10     2278   563. 1909.   -194.         -1725.        1704.
      -#> # ℹ 6,365 more rows
      -#> # ℹ 3 more variables: anomaly <chr>, recomposed_l1 <dbl>, recomposed_l2 <dbl>
      -
      -
      -
      -
      -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/reference/time_scale_template.html b/docs/reference/time_scale_template.html deleted file mode 100644 index 613e28e..0000000 --- a/docs/reference/time_scale_template.html +++ /dev/null @@ -1,147 +0,0 @@ - -Get and modify time scale template — set_time_scale_template • anomalize - - -
      -
      - - - -
      -
      - - -
      -

      Get and modify time scale template

      -
      - -
      -
      set_time_scale_template(data)
      -
      -get_time_scale_template()
      -
      -time_scale_template()
      -
      - -
      -

      Arguments

      -
      data
      -

      A tibble with a "time_scale", "frequency", and "trend" columns.

      - -
      -
      -

      Details

      -

      Used to get and set the time scale template, which is used by time_frequency() -and time_trend() when period = "auto".

      -
      -
      -

      See also

      - -
      - -
      -

      Examples

      -
      
      -get_time_scale_template()
      -#> # A tibble: 8 × 3
      -#>   time_scale frequency trend   
      -#>   <chr>      <chr>     <chr>   
      -#> 1 second     1 hour    12 hours
      -#> 2 minute     1 day     14 days 
      -#> 3 hour       1 day     1 month 
      -#> 4 day        1 week    3 months
      -#> 5 week       1 quarter 1 year  
      -#> 6 month      1 year    5 years 
      -#> 7 quarter    1 year    10 years
      -#> 8 year       5 years   30 years
      -
      -set_time_scale_template(time_scale_template())
      -
      -
      -
      -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -
      - - - - - - - - diff --git a/docs/sitemap.xml b/docs/sitemap.xml deleted file mode 100644 index d0a25a2..0000000 --- a/docs/sitemap.xml +++ /dev/null @@ -1,72 +0,0 @@ - - - - /404.html - - - /articles/anomalize_methods.html - - - /articles/anomalize_quick_start_guide.html - - - /articles/forecasting_with_cleaned_anomalies.html - - - /articles/index.html - - - /authors.html - - - /index.html - - - /news/index.html - - - /reference/anomalize.html - - - /reference/anomalize_methods.html - - - /reference/anomalize_package.html - - - /reference/clean_anomalies.html - - - /reference/decompose_methods.html - - - /reference/index.html - - - /reference/plot_anomalies.html - - - /reference/plot_anomaly_decomposition.html - - - /reference/prep_tbl_time.html - - - /reference/tidyverse_cran_downloads.html - - - /reference/time_apply.html - - - /reference/time_decompose.html - - - /reference/time_frequency.html - - - /reference/time_recompose.html - - - /reference/time_scale_template.html - - From dd42e9c48401b5813e5f9eee4d208c4e502dee60 Mon Sep 17 00:00:00 2001 From: olivroy Date: Wed, 6 Dec 2023 09:30:14 -0500 Subject: [PATCH 4/4] ignore docs --- .gitignore | 2 ++ 1 file changed, 2 insertions(+) diff --git a/.gitignore b/.gitignore index 1fb9175..88437f4 100644 --- a/.gitignore +++ b/.gitignore @@ -7,3 +7,5 @@ doc Meta /doc/ /Meta/ +docs +.DS_Store
      -
      - - - -
      -
      - - -
      -

      Visualize the anomalies in one or multiple time series

      -
      - -
      -
      plot_anomalies(
      -  data,
      -  time_recomposed = FALSE,
      -  ncol = 1,
      -  color_no = "#2c3e50",
      -  color_yes = "#e31a1c",
      -  fill_ribbon = "grey70",
      -  alpha_dots = 1,
      -  alpha_circles = 1,
      -  alpha_ribbon = 1,
      -  size_dots = 1.5,
      -  size_circles = 4
      -)
      -
      - -
      -

      Arguments

      -
      data
      -

      A tibble or tbl_time object.

      - - -
      time_recomposed
      -

      A boolean. If TRUE, will use the time_recompose() bands to -place bands as approximate limits around the "normal" data.

      - - -
      ncol
      -

      Number of columns to display. Set to 1 for single column by default.

      - - -
      color_no
      -

      Color for non-anomalous data.

      - - -
      color_yes
      -

      Color for anomalous data.

      - - -
      fill_ribbon
      -

      Fill color for the time_recomposed ribbon.

      - - -
      alpha_dots
      -

      Controls the transparency of the dots. Reduce when too many dots on the screen.

      - - -
      alpha_circles
      -

      Controls the transparency of the circles that identify anomalies.

      - - -
      alpha_ribbon
      -

      Controls the transparency of the time_recomposed ribbon.

      - - -
      size_dots
      -

      Controls the size of the dots.

      - - -
      size_circles
      -

      Controls the size of the circles that identify anomalies.

      - -
      -
      -

      Value

      - - -

      Returns a ggplot object.

      -
      -
      -

      Details

      -

      Plotting function for visualizing anomalies on one or more time series. -Multiple time series must be grouped using dplyr::group_by().

      -
      - - -
      -

      Examples

      -
      
      -if (FALSE) {
      -library(dplyr)
      -library(ggplot2)
      -
      -data(tidyverse_cran_downloads)
      -
      -#### SINGLE TIME SERIES ####
      -tidyverse_cran_downloads %>%
      -    filter(package == "tidyquant") %>%
      -    ungroup() %>%
      -    time_decompose(count, method = "stl") %>%
      -    anomalize(remainder, method = "iqr") %>%
      -    time_recompose() %>%
      -    plot_anomalies(time_recomposed = TRUE)
      -
      -
      -#### MULTIPLE TIME SERIES ####
      -tidyverse_cran_downloads %>%
      -    time_decompose(count, method = "stl") %>%
      -    anomalize(remainder, method = "iqr") %>%
      -    time_recompose() %>%
      -    plot_anomalies(time_recomposed = TRUE, ncol = 3)
      -}
      -
      -
      -
      -
      - -
      - - -
      - -
      -

      Site built with pkgdown 2.0.7.

      -
      - -

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    4. -
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      -

      Site built with pkgdown 2.0.7.

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      - -
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      - - - - -
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      - - - - -
      -

      Forecasting error can often be reduced 20% to 50% by repairing -anomolous data

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      -

      Example - Reducing Forecasting Error by 32% -

      -

      We can often get better forecast performance by cleaning anomalous -data prior to forecasting. This is the perfect use case for integrating -the clean_anomalies() function into your -forecast workflow.

      -
      -library(tidyverse)
      -library(tidyquant)
      -library(anomalize)
      -library(timetk)
      -
      -# NOTE: timetk now has anomaly detection built in, which 
      -#  will get the new functionality going forward.
      -#  Use this script to prevent overwriting legacy anomalize:
      -
      -anomalize <- anomalize::anomalize
      -plot_anomalies <- anomalize::plot_anomalies
      -

      Here is a short example with the -tidyverse_cran_downloads dataset that comes with -anomalize. We’ll see how we can reduce the forecast -error by 32% simply by repairing anomalies.

      -
      -tidyverse_cran_downloads
      -#> # A tibble: 6,375 × 3
      -#> # Groups:   package [15]
      -#>    date       count package
      -#>    <date>     <dbl> <chr>  
      -#>  1 2017-01-01   873 tidyr  
      -#>  2 2017-01-02  1840 tidyr  
      -#>  3 2017-01-03  2495 tidyr  
      -#>  4 2017-01-04  2906 tidyr  
      -#>  5 2017-01-05  2847 tidyr  
      -#>  6 2017-01-06  2756 tidyr  
      -#>  7 2017-01-07  1439 tidyr  
      -#>  8 2017-01-08  1556 tidyr  
      -#>  9 2017-01-09  3678 tidyr  
      -#> 10 2017-01-10  7086 tidyr  
      -#> # ℹ 6,365 more rows
      -

      Let’s take one package with some extreme events. We can hone in on -lubridate, which has some outliers that we can fix.

      -
      -tidyverse_cran_downloads %>%
      -  ggplot(aes(date, count, color = package)) +
      -  geom_point(alpha = 0.5) +
      -  facet_wrap(~ package, ncol = 3, scales = "free_y") +
      -  scale_color_viridis_d() +
      -  theme_tq() 
      -

      -
      -
      -

      Forecasting Lubridate Downloads -

      -

      Let’s focus on downloads of the lubridate R package.

      -
      -lubridate_tbl <- tidyverse_cran_downloads %>%
      -  ungroup() %>%
      -  filter(package == "lubridate")
      -

      First, we’ll make a function, forecast_mae(), that can -take the input of both cleaned and uncleaned anomalies and calculate -forecast error of future uncleaned anomalies.

      -

      The modeling function uses the following criteria:

      -
        -
      • Split the data into training and testing data that -maintains the correct time-series sequence using the prop -argument.
      • -
      • Models the daily time series of the training data set from observed -(demonstrates no cleaning) or observed and cleaned (demonstrates -improvement from cleaning). Specified by the col_train -argument.
      • -
      • Compares the predictions to the observed values. Specified by the -col_test argument.
      • -
      -
      -forecast_mae <- function(data, col_train, col_test, prop = 0.8) {
      -  
      -  predict_expr <- enquo(col_train)
      -  actual_expr <- enquo(col_test)
      -  
      -  idx_train <- 1:(floor(prop * nrow(data)))
      -  
      -  train_tbl <- data %>% filter(row_number() %in% idx_train)
      -  test_tbl  <- data %>% filter(!row_number() %in% idx_train)
      -  
      -  # Model using training data (training) 
      -  model_formula <- as.formula(paste0(quo_name(predict_expr), " ~ index.num + year + quarter + month.lbl + day + wday.lbl"))
      -  
      -  model_glm <- train_tbl %>%
      -    tk_augment_timeseries_signature() %>%
      -    glm(model_formula, data = .)
      -  
      -  # Make Prediction
      -  suppressWarnings({
      -    # Suppress rank-deficit warning
      -    prediction <- predict(model_glm, newdata = test_tbl %>% tk_augment_timeseries_signature()) 
      -    actual     <- test_tbl %>% pull(!! actual_expr)
      -  })
      -  
      -  # Calculate MAE
      -  mae <- mean(abs(prediction - actual))
      -  
      -  return(mae)
      -  
      -}
      -
      -
      -

      Workflow for Cleaning Anomalies -

      -

      We will use the anomalize workflow of decomposing -(time_decompose()) and identifying anomalies -(anomalize()). We use the function, -clean_anomalies(), to add new column called -“observed_cleaned” that is repaired by replacing all anomalies with the -trend + seasonal components from the decompose operation. We -can now experiment to see the improvment in forecasting performance by -comparing a forecast made with “observed” versus “observed_cleaned”

      -
      -lubridate_anomalized_tbl <- lubridate_tbl %>%
      -  time_decompose(count) %>%
      -  anomalize(remainder) %>%
      -  
      -  # Function to clean & repair anomalous data
      -  clean_anomalies()
      -#> Converting from tbl_df to tbl_time.
      -#> Auto-index message: index = date
      -#> frequency = 7 days
      -#> trend = 91 days
      -
      -lubridate_anomalized_tbl
      -#> # A time tibble: 425 × 9
      -#> # Index:         date
      -#>    date       observed season trend remainder remainder_l1 remainder_l2 anomaly
      -#>    <date>        <dbl>  <dbl> <dbl>     <dbl>        <dbl>        <dbl> <chr>  
      -#>  1 2017-01-01      643 -2078. 2474.      246.       -3323.        3310. No     
      -#>  2 2017-01-02     1350   518. 2491.    -1659.       -3323.        3310. No     
      -#>  3 2017-01-03     2940  1117. 2508.     -685.       -3323.        3310. No     
      -#>  4 2017-01-04     4269  1220. 2524.      525.       -3323.        3310. No     
      -#>  5 2017-01-05     3724   865. 2541.      318.       -3323.        3310. No     
      -#>  6 2017-01-06     2326   356. 2558.     -588.       -3323.        3310. No     
      -#>  7 2017-01-07     1107 -1998. 2574.      531.       -3323.        3310. No     
      -#>  8 2017-01-08     1058 -2078. 2591.      545.       -3323.        3310. No     
      -#>  9 2017-01-09     2494   518. 2608.     -632.       -3323.        3310. No     
      -#> 10 2017-01-10     3237  1117. 2624.     -504.       -3323.        3310. No     
      -#> # ℹ 415 more rows
      -#> # ℹ 1 more variable: observed_cleaned <dbl>
      -
      -
      -

      Before Cleaning with anomalize -

      -
      -lubridate_anomalized_tbl %>%
      -  forecast_mae(col_train = observed, col_test = observed, prop = 0.8)
      -#> tk_augment_timeseries_signature(): Using the following .date_var variable: date
      -#> tk_augment_timeseries_signature(): Using the following .date_var variable: date
      -#> [1] 4054.053
      -
      -
      -

      After Cleaning with anomalize -

      -
      -lubridate_anomalized_tbl %>%
      -  forecast_mae(col_train = observed_cleaned, col_test = observed, prop = 0.8)
      -#> tk_augment_timeseries_signature(): Using the following .date_var variable: date
      -#> tk_augment_timeseries_signature(): Using the following .date_var variable: date
      -#> [1] 2755.297
      -
      -
      -

      32% Reduction in Forecast Error -

      -

      This is approximately a 32% reduction in forecast error as measure by -Mean Absolute Error (MAE).

      -
      -(2755 - 4054) / 4054 
      -#> [1] -0.3204243
      -
      -
      -

      Interested in Learning Anomaly Detection? -

      -

      Business Science offers two 1-hour courses on Anomaly Detection:

      - -
      -
      - - - -
      - - - -
      - -
      -

      -

      Site built with pkgdown 2.0.7.

      -
      - -
      -

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    -
    - - - - -
    -
    - - - - -

    The anomalize package is a feature rich package for -performing anomaly detection. It’s geared towards time series analysis, -which is one of the biggest needs for understanding when anomalies -occur. We have a quick start section called “5-Minutes to Anomalize” for -those looking to jump right in. We also have a detailed section on -parameter adjustment for those looking to understand what nobs they can -turn. Finally, for those really looking to get under the hood, we have -another vignette called “Anomalize Methods” that gets into a deep -discussion on STL, Twitter, IQR and GESD methods that are used to power -anomalize.

    -
    -

    Anomalize Intro on YouTube -

    -

    As a first step, you may wish to watch our anomalize -introduction video on YouTube.

    -

    Anomalize

    -

    Check out our entire Software -Intro Series on YouTube!

    -
    -
    -

    5-Minutes To Anomalize -

    -

    Load libraries.

    -
    -library(tidyverse)
    -library(tibbletime)
    -library(anomalize)
    -
    -# NOTE: timetk now has anomaly detection built in, which 
    -#  will get the new functionality going forward.
    -
    -anomalize <- anomalize::anomalize
    -plot_anomalies <- anomalize::plot_anomalies
    -

    Get some data. We’ll use the tidyverse_cran_downloads -data set that comes with anomalize. A few points:

    -
      -
    • It’s a tibbletime object (class -tbl_time), which is the object structure that -anomalize works with because it’s time aware! Tibbles -(class tbl_df) will automatically be converted.

    • -
    • It contains daily download counts on 15 “tidy” packages spanning -2017-01-01 to 2018-03-01. The 15 packages are already grouped for your -convenience.

    • -
    • It’s all setup and ready to analyze with -anomalize!

    • -
    -
    -tidyverse_cran_downloads
    -#> # A time tibble: 6,375 × 3
    -#> # Index:         date
    -#> # Groups:        package [15]
    -#>    date       count package
    -#>    <date>     <dbl> <chr>  
    -#>  1 2017-01-01   873 tidyr  
    -#>  2 2017-01-02  1840 tidyr  
    -#>  3 2017-01-03  2495 tidyr  
    -#>  4 2017-01-04  2906 tidyr  
    -#>  5 2017-01-05  2847 tidyr  
    -#>  6 2017-01-06  2756 tidyr  
    -#>  7 2017-01-07  1439 tidyr  
    -#>  8 2017-01-08  1556 tidyr  
    -#>  9 2017-01-09  3678 tidyr  
    -#> 10 2017-01-10  7086 tidyr  
    -#> # ℹ 6,365 more rows
    -

    We can use the general workflow for anomaly detection, which involves -three main functions:

    -
      -
    1. -time_decompose(): Separates the time series into -seasonal, trend, and remainder components
    2. -
    3. -anomalize(): Applies anomaly detection methods to the -remainder component.
    4. -
    5. -time_recompose(): Calculates limits that separate the -“normal” data from the anomalies!
    6. -
    -
    -tidyverse_cran_downloads_anomalized <- tidyverse_cran_downloads %>%
    -    time_decompose(count, merge = TRUE) %>%
    -    anomalize(remainder) %>%
    -    time_recompose()
    -#> Registered S3 method overwritten by 'quantmod':
    -#>   method            from
    -#>   as.zoo.data.frame zoo
    -
    -tidyverse_cran_downloads_anomalized %>% glimpse()
    -#> Rows: 6,375
    -#> Columns: 12
    -#> Index: date
    -#> Groups: package [15]
    -#> $ package       <chr> "broom", "broom", "broom", "broom", "broom", "broom", "b…
    -#> $ date          <date> 2017-01-01, 2017-01-02, 2017-01-03, 2017-01-04, 2017-01…
    -#> $ count         <dbl> 1053, 1481, 1851, 1947, 1927, 1948, 1542, 1479, 2057, 22…
    -#> $ observed      <dbl> 1.053000e+03, 1.481000e+03, 1.851000e+03, 1.947000e+03, …
    -#> $ season        <dbl> -1006.9759, 339.6028, 562.5794, 526.0532, 430.1275, 136.…
    -#> $ trend         <dbl> 1708.465, 1730.742, 1753.018, 1775.294, 1797.571, 1819.8…
    -#> $ remainder     <dbl> 351.510801, -589.344328, -464.597345, -354.347509, -300.…
    -#> $ remainder_l1  <dbl> -1724.778, -1724.778, -1724.778, -1724.778, -1724.778, -…
    -#> $ remainder_l2  <dbl> 1704.371, 1704.371, 1704.371, 1704.371, 1704.371, 1704.3…
    -#> $ anomaly       <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", "N…
    -#> $ recomposed_l1 <dbl> -1023.2887, 345.5664, 590.8195, 576.5696, 502.9204, 231.…
    -#> $ recomposed_l2 <dbl> 2405.860, 3774.715, 4019.968, 4005.718, 3932.069, 3660.4…
    -

    Let’s explain what happened:

    -
      -
    1. -time_decompose(count, merge = TRUE): This performs a -time series decomposition on the “count” column using seasonal -decomposition. It created four columns: -
        -
      • “observed”: The observed values (actuals)
      • -
      • “season”: The seasonal or cyclic trend. The default for daily data -is a weekly seasonality.
      • -
      • “trend”: This is the long term trend. The default is a Loess -smoother using spans of 3-months for daily data.
      • -
      • “remainder”: This is what we want to analyze for outliers. It is -simply the observed minus both the season and trend.
      • -
      • Setting merge = TRUE keeps the original data with the -newly created columns.
      • -
      -
    2. -
    3. -anomalize(remainder): This performs anomaly detection -on the remainder column. It creates three new columns: -
        -
      • “remainder_l1”: The lower limit of the remainder
      • -
      • “remainder_l2”: The upper limit of the remainder
      • -
      • “anomaly”: Yes/No telling us whether or not the observation is an -anomaly
      • -
      -
    4. -
    5. -time_recompose(): This recomposes the season, trend and -remainder_l1 and remainder_l2 columns into new limits that bound the -observed values. The two new columns created are: -
        -
      • “recomposed_l1”: The lower bound of outliers around the observed -value
      • -
      • “recomposed_l2”: The upper bound of outliers around the observed -value
      • -
      -
    6. -
    -

    We can then visualize the anomalies using the -plot_anomalies() function.

    -
    -tidyverse_cran_downloads_anomalized %>%
    -    plot_anomalies(ncol = 3, alpha_dots = 0.25)
    -

    -
    -
    -

    Parameter Adjustment -

    -

    Now that you have an overview of the package, you can begin to adjust -the parameter settings. The first settings you may wish to explore are -related to time series decomposition: trend and seasonality. The second -are related to anomaly detection: alpha and max anoms.

    -
    -

    Adjusting Decomposition Trend and Seasonality -

    -

    Adjusting the trend and seasonality are fundamental to time series -analysis and specifically time series decomposition. With -anomalize, it’s simple to make adjustments because -everything is done with date or datetime information so you can -intuitively select increments by time spans that make sense (e.g. “5 -minutes” or “1 month”).

    -

    To get started, let’s isolate one of the time series packages: -lubridate.

    -
    -lubridate_daily_downloads <- tidyverse_cran_downloads %>%
    -    filter(package == "lubridate") %>%
    -    ungroup()
    -
    -lubridate_daily_downloads
    -#> # A time tibble: 425 × 3
    -#> # Index:         date
    -#>    date       count package  
    -#>    <date>     <dbl> <chr>    
    -#>  1 2017-01-01   643 lubridate
    -#>  2 2017-01-02  1350 lubridate
    -#>  3 2017-01-03  2940 lubridate
    -#>  4 2017-01-04  4269 lubridate
    -#>  5 2017-01-05  3724 lubridate
    -#>  6 2017-01-06  2326 lubridate
    -#>  7 2017-01-07  1107 lubridate
    -#>  8 2017-01-08  1058 lubridate
    -#>  9 2017-01-09  2494 lubridate
    -#> 10 2017-01-10  3237 lubridate
    -#> # ℹ 415 more rows
    -

    Next, let’s perform anomaly detection.

    -
    -lubridate_daily_downloads_anomalized <- lubridate_daily_downloads %>% 
    -    time_decompose(count) %>%
    -    anomalize(remainder) %>%
    -    time_recompose()
    -#> frequency = 7 days
    -#> trend = 91 days
    -
    -lubridate_daily_downloads_anomalized %>% glimpse()
    -#> Rows: 425
    -#> Columns: 10
    -#> Index: date
    -#> $ date          <date> 2017-01-01, 2017-01-02, 2017-01-03, 2017-01-04, 2017-01…
    -#> $ observed      <dbl> 6.430000e+02, 1.350000e+03, 2.940000e+03, 4.269000e+03, …
    -#> $ season        <dbl> -2077.6548, 517.9370, 1117.0490, 1219.5377, 865.1171, 35…
    -#> $ trend         <dbl> 2474.491, 2491.126, 2507.761, 2524.397, 2541.032, 2557.6…
    -#> $ remainder     <dbl> 246.1636, -1659.0632, -684.8105, 525.0657, 317.8511, -58…
    -#> $ remainder_l1  <dbl> -3323.425, -3323.425, -3323.425, -3323.425, -3323.425, -…
    -#> $ remainder_l2  <dbl> 3310.268, 3310.268, 3310.268, 3310.268, 3310.268, 3310.2…
    -#> $ anomaly       <chr> "No", "No", "No", "No", "No", "No", "No", "No", "No", "N…
    -#> $ recomposed_l1 <dbl> -2926.58907, -314.36218, 301.38509, 420.50889, 82.72349,…
    -#> $ recomposed_l2 <dbl> 3707.105, 6319.331, 6935.079, 7054.202, 6716.417, 6223.8…
    -

    First, notice that a frequency and a trend -were automatically selected for us. This is by design. The arguments -frequency = "auto" and trend = "auto" are the -defaults. We can visualize this decomposition using -plot_anomaly_decomposition().

    -
    -p1 <- lubridate_daily_downloads_anomalized %>%
    -    plot_anomaly_decomposition() +
    -    ggtitle("Freq/Trend = 'auto'")
    -
    -p1
    -

    -

    When “auto” is used, a get_time_scale_template() is used -to determine logical frequency and trend spans based on the scale of the -data. You can uncover the logic:

    -
    -get_time_scale_template()
    -#> # A tibble: 8 × 3
    -#>   time_scale frequency trend   
    -#>   <chr>      <chr>     <chr>   
    -#> 1 second     1 hour    12 hours
    -#> 2 minute     1 day     14 days 
    -#> 3 hour       1 day     1 month 
    -#> 4 day        1 week    3 months
    -#> 5 week       1 quarter 1 year  
    -#> 6 month      1 year    5 years 
    -#> 7 quarter    1 year    10 years
    -#> 8 year       5 years   30 years
    -

    What this means is that if the scale is 1 day (meaning the difference -between each data point is 1 day), then the frequency will be 7 days (or -1 week) and the trend will be around 90 days (or 3 months). This logic -tends to work quite well for anomaly detection, but you may wish to -adjust it. There are two ways:

    -
      -
    1. Local parameter adjustment
    2. -
    3. Global parameter adjustment
    4. -
    -
    -
    Local Parameter Adjustment -
    -

    Local parameter adjustment can be performed by tweaking the -in-function parameters. Below we adjust trend = "14 days" -which makes for a quite overfit trend.

    -
    -# Local adjustment via time_decompose
    -p2 <- lubridate_daily_downloads %>%
    -    time_decompose(count,
    -                   frequency = "auto",
    -                   trend     = "14 days") %>%
    -    anomalize(remainder) %>%
    -    plot_anomaly_decomposition() +
    -    ggtitle("Trend = 14 Days (Local)")
    -#> frequency = 7 days
    -#> trend = 14 days
    -
    -# Show plots
    -p1
    -p2
    -

    -
    -
    -
    Global Parameter Adjustement -
    -

    We can also adjust globally by using -set_time_scale_template() to update the default template to -one that we prefer. We’ll change the “3 month” trend to “2 weeks” for -time scale = “day”. Use time_scale_template() to retrieve -the time scale template that anomalize begins with, them -mutate() the trend field in the desired location, and use -set_time_scale_template() to update the template in the -global options. We can retrieve the updated template using -get_time_scale_template() to verify the change has been -executed properly.

    -
    -# Globally change time scale template options
    -time_scale_template() %>%
    -    mutate(trend = ifelse(time_scale == "day", "14 days", trend)) %>%
    -    set_time_scale_template()
    -
    -get_time_scale_template()
    -#> # A tibble: 8 × 3
    -#>   time_scale frequency trend   
    -#>   <chr>      <chr>     <chr>   
    -#> 1 second     1 hour    12 hours
    -#> 2 minute     1 day     14 days 
    -#> 3 hour       1 day     1 month 
    -#> 4 day        1 week    14 days 
    -#> 5 week       1 quarter 1 year  
    -#> 6 month      1 year    5 years 
    -#> 7 quarter    1 year    10 years
    -#> 8 year       5 years   30 years
    -

    Finally we can re-run the time_decompose() with -defaults, and we can see that the trend is “14 days”.

    -
    -p3 <- lubridate_daily_downloads %>%
    -    time_decompose(count) %>%
    -    anomalize(remainder) %>%
    -    plot_anomaly_decomposition() +
    -    ggtitle("Trend = 14 Days (Global)")
    -#> frequency = 7 days
    -#> trend = 14 days
    -
    -p3
    -

    -

    Let’s reset the time scale template defaults back to the original -defaults.

    -
    -# Set time scale template to the original defaults
    -time_scale_template() %>%
    -    set_time_scale_template()
    -
    -# Verify the change
    -get_time_scale_template()
    -#> # A tibble: 8 × 3
    -#>   time_scale frequency trend   
    -#>   <chr>      <chr>     <chr>   
    -#> 1 second     1 hour    12 hours
    -#> 2 minute     1 day     14 days 
    -#> 3 hour       1 day     1 month 
    -#> 4 day        1 week    3 months
    -#> 5 week       1 quarter 1 year  
    -#> 6 month      1 year    5 years 
    -#> 7 quarter    1 year    10 years
    -#> 8 year       5 years   30 years
    -
    -
    -
    -

    Adjusting Anomaly Detection Alpha and Max Anoms -

    -

    The alpha and max_anoms are the two -parameters that control the anomalize() function. Here’s -how they work.

    -
    -
    Alpha -
    -

    We can adjust alpha, which is set to 0.05 by default. By -default the bands just cover the outside of the range.

    -
    -p4 <- lubridate_daily_downloads %>%
    -    time_decompose(count) %>%
    -    anomalize(remainder, alpha = 0.05, max_anoms = 0.2) %>%
    -    time_recompose() %>%
    -    plot_anomalies(time_recomposed = TRUE) +
    -    ggtitle("alpha = 0.05")
    -#> frequency = 7 days
    -#> trend = 91 days
    -
    -p4
    -

    -

    We can decrease alpha, which increases the bands making -it more difficult to be an outlier. See that the bands doubled in -size.

    -
    -p5 <- lubridate_daily_downloads %>%
    -    time_decompose(count) %>%
    -    anomalize(remainder, alpha = 0.025, max_anoms = 0.2) %>%
    -    time_recompose() %>%
    -    plot_anomalies(time_recomposed = TRUE) +
    -    ggtitle("alpha = 0.025")
    -#> frequency = 7 days
    -#> trend = 91 days
    -
    -p4 
    -p5
    -

    -
    -
    -
    Max Anoms -
    -

    The max_anoms parameter is used to control the maximum -percentage of data that can be an anomaly. This is useful in cases where -alpha is too difficult to tune, and you really want to -focus on the most aggregious anomalies.

    -

    Let’s adjust alpha = 0.3 so pretty much anything is an -outlier. Now let’s try a comparison between max_anoms = 0.2 -(20% anomalies allowed) and max_anoms = 0.05 (5% anomalies -allowed).

    -
    -p6 <- lubridate_daily_downloads %>%
    -    time_decompose(count) %>%
    -    anomalize(remainder, alpha = 0.3, max_anoms = 0.2) %>%
    -    time_recompose() %>%
    -    plot_anomalies(time_recomposed = TRUE) +
    -    ggtitle("20% Anomalies")
    -#> frequency = 7 days
    -#> trend = 91 days
    -
    -p7 <- lubridate_daily_downloads %>%
    -    time_decompose(count) %>%
    -    anomalize(remainder, alpha = 0.3, max_anoms = 0.05) %>%
    -    time_recompose() %>%
    -    plot_anomalies(time_recomposed = TRUE) +
    -    ggtitle("5% Anomalies")
    -#> frequency = 7 days
    -#> trend = 91 days
    -
    -p6
    -p7
    -

    -

    In reality, you’ll probably want to leave alpha in the -range of 0.10 to 0.02, but it makes a nice illustration of how you can -also use max_anoms to ensure only the most aggregious -anomalies are identified.

    -
    -
    -
    -
    -

    Further Understanding: Methods -

    -

    If you haven’t had your fill and want to dive into the methods that -power anomalize, check out the vignette, “Anomalize Methods”.

    -
    -
    -

    Interested in Learning Anomaly Detection? -

    -

    Business Science offers two 1-hour courses on Anomaly Detection:

    - -
    -
    - - - -
    - - - -
    - -
    -

    -

    Site built with pkgdown 2.0.7.

    -
    - -
    -

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