diff --git a/R/arima.R b/R/arima.R
index 1f30d004..2bcc5419 100644
--- a/R/arima.R
+++ b/R/arima.R
@@ -964,3 +964,30 @@ is.Arima <- function(x) {
 fitted.ar <- function(object, ...) {
   getResponse(object) - residuals(object)
 }
+
+hfitted.Arima <- function(object, h, ...) {
+  # As implemented in Fable
+  if(h == 1){
+    return(object$fitted)
+  }
+  y <- object$fitted+residuals(object, "innovation")
+  yx <- residuals(object, "regression")
+  # Get fitted model
+  mod <- object$model
+  # Reset model to initial state
+  mod <-  stats::makeARIMA(mod$phi, mod$theta, mod$Delta)
+  # Calculate regression component
+  xm <- y - yx
+  fits <- rep_len(NA_real_, length(y))
+  
+  start <- length(mod$Delta) + 1
+  end <- length(yx) - h
+  idx <- if(start > end) integer(0L) else start:end
+  for(i in idx) {
+    fc_mod <- attr(stats::KalmanRun(yx[seq_len(i)], mod, update = TRUE), "mod")
+    fits[i + h] <- stats::KalmanForecast(h, fc_mod)$pred[h] + xm[i+h]
+  }
+  fits <- ts(fits)
+  tsp(fits) <- tsp(object$x)
+  fits
+}
diff --git a/R/forecast.R b/R/forecast.R
index c1ef40da..a01ef319 100644
--- a/R/forecast.R
+++ b/R/forecast.R
@@ -451,6 +451,10 @@ predict.default <- function(object, ...) {
 }
 
 hfitted <- function(object, h=1, FUN=NULL, ...) {
+  UseMethod("hfitted")
+}
+
+hfitted.default <- function(object, h=1, FUN=NULL, ...) {
   if (h == 1) {
     return(fitted(object))
   }