diff --git a/R/step_training_window.R b/R/step_training_window.R
index eafc076c..812797d7 100644
--- a/R/step_training_window.R
+++ b/R/step_training_window.R
@@ -8,6 +8,17 @@
 #' @param n_recent An integer value that represents the number of most recent
 #'   observations that are to be kept in the training window per key
 #'   The default value is 50.
+#' @param seasonal Bool, default FALSE. If TRUE, the training window will slice
+#'   through epidemic seasons. This is useful for forecasting models that need
+#'   to leverage the data in previous years, but only limited to similar phases
+#'   in the epidemic. Most useful to heavily seasonal data, like influenza.
+#'   Expects n_recent to be finite.
+#' @param seasonal_forward_window An integer value that represents the number of days
+#'   after a season week to include in the training window. The default value
+#'   is 14. Only valid when seasonal is TRUE.
+#' @param seasonal_backward_window An integer value that represents the number of days
+#'   before a season week to include in the training window. The default value
+#'   is 35. Only valid when seasonal is TRUE.
 #' @param epi_keys An optional character vector for specifying "key" variables
 #'   to group on. The default, `NULL`, ensures that every key combination is
 #'   limited.
@@ -42,10 +53,13 @@ step_training_window <-
   function(recipe,
            role = NA,
            n_recent = 50,
+           seasonal = FALSE,
+           seasonal_forward_window = 14,
+           seasonal_backward_window = 35,
            epi_keys = NULL,
            id = rand_id("training_window")) {
-    arg_is_scalar(n_recent, id)
-    arg_is_pos(n_recent)
+    arg_is_scalar(n_recent, id, seasonal, seasonal_forward_window, seasonal_backward_window)
+    arg_is_pos(n_recent, seasonal_forward_window, seasonal_backward_window)
     if (is.finite(n_recent)) arg_is_pos_int(n_recent)
     arg_is_chr(id)
     arg_is_chr(epi_keys, allow_null = TRUE)
@@ -55,6 +69,9 @@ step_training_window <-
         role = role,
         trained = FALSE,
         n_recent = n_recent,
+        seasonal = seasonal,
+        seasonal_forward_window = seasonal_forward_window,
+        seasonal_backward_window = seasonal_backward_window,
         epi_keys = epi_keys,
         skip = TRUE,
         id = id
@@ -63,12 +80,15 @@ step_training_window <-
   }
 
 step_training_window_new <-
-  function(role, trained, n_recent, epi_keys, skip, id) {
+  function(role, trained, n_recent, seasonal, seasonal_forward_window, seasonal_backward_window, epi_keys, skip, id) {
     step(
       subclass = "training_window",
       role = role,
       trained = trained,
       n_recent = n_recent,
+      seasonal = seasonal,
+      seasonal_forward_window = seasonal_forward_window,
+      seasonal_backward_window = seasonal_backward_window,
       epi_keys = epi_keys,
       skip = skip,
       id = id
@@ -86,6 +106,9 @@ prep.step_training_window <- function(x, training, info = NULL, ...) {
     role = x$role,
     trained = TRUE,
     n_recent = x$n_recent,
+    seasonal = x$seasonal,
+    seasonal_forward_window = x$seasonal_forward_window,
+    seasonal_backward_window = x$seasonal_backward_window,
     epi_keys = ek,
     skip = x$skip,
     id = x$id
@@ -104,15 +127,49 @@ bake.step_training_window <- function(object, new_data, ...) {
       ungroup()
   }
 
+  if (object$seasonal) {
+    # TODO: This needs to take into account different time types of time_value.
+    # Currently, it assumes time_value is a Date.
+    new_data <- new_data %>% add_season_info()
+    last_data_season_week <- new_data %>%
+      filter(time_value == max(time_value)) %>%
+      pull(season_week) %>%
+      max()
+    recent_weeks <- c(last_data_season_week)
+    if (inherits(new_data, "epi_df")) {
+      current_season_week <- convert_epiweek_to_season_week(epiyear(epi_as_of(new_data)), epiweek(epi_as_of(new_data)))
+      recent_weeks <- c(recent_weeks, current_season_week)
+    }
+    date_ranges <- new_data %>%
+      filter(season_week %in% recent_weeks) %>%
+      pull(time_value) %>%
+      unique() %>%
+      map(~ c(.x - 1:(object$seasonal_backward_window), .x + 0:(object$seasonal_forward_window))) %>%
+      unlist() %>%
+      as.Date() %>%
+      unique()
+    new_data %<>% filter(time_value %in% date_ranges)
+  }
+
+
   new_data
 }
 
 #' @export
 print.step_training_window <-
   function(x, width = max(20, options()$width - 30), ...) {
-    title <- "# of recent observations per key limited to:"
-    n_recent <- x$n_recent
-    tr_obj <- recipes::format_selectors(rlang::enquos(n_recent), width)
-    recipes::print_step(tr_obj, rlang::enquos(n_recent), x$trained, title, width)
+    if (x$seasonal) {
+      title <- "# of seasonal observations per key limited to:"
+      n_recent <- x$n_recent
+      seasonal_forward_window <- x$seasonal_forward_window
+      seasonal_backward_window <- x$seasonal_backward_window
+      tr_obj <- recipes::format_selectors(rlang::enquos(n_recent, seasonal_forward_window, seasonal_backward_window), width)
+      recipes::print_step(tr_obj, rlang::enquos(n_recent, seasonal_forward_window, seasonal_backward_window), x$trained, title, width)
+    } else {
+      title <- "# of recent observations per key limited to:"
+      n_recent <- x$n_recent
+      tr_obj <- recipes::format_selectors(rlang::enquos(n_recent), width)
+      recipes::print_step(tr_obj, rlang::enquos(n_recent), x$trained, title, width)
+    }
     invisible(x)
   }