Allow shifting the start date of the weekly seasonality plot

R/R/prophet.R

@@ -987,13 +987,16 @@ plot.prophet <- function(x, fcst, uncertainty = TRUE, plot_cap = TRUE,
 #'  plotted for the trend, from fcst columns trend_lower and trend_upper.
 #' @param plot_cap Boolean indicating if the capacity should be shown in the
 #'  figure, if available.
+#' @param weekly_start Integer specifying the start day of the weekly
+#'  seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day
+#'  to Monday, and so on.
 #'
 #' @return Invisibly return a list containing the plotted ggplot objects
 #'
 #' @export
 #' @importFrom dplyr "%>%"
 prophet_plot_components <- function(m, fcst, uncertainty = TRUE,
-                                    plot_cap = TRUE) {
+                                    plot_cap = TRUE, weekly_start = 0) {
   df <- df_for_plotting(m, fcst)
   # Plot the trend
   panels <- list(plot_trend(df, uncertainty, plot_cap))
@@ -1003,7 +1006,7 @@ prophet_plot_components <- function(m, fcst, uncertainty = TRUE,
   }
   # Plot weekly seasonality, if present
   if ("weekly" %in% colnames(df)) {
-    panels[[length(panels) + 1]] <- plot_weekly(m, uncertainty)
+    panels[[length(panels) + 1]] <- plot_weekly(m, uncertainty, weekly_start)
   }
   # Plot yearly seasonality, if present
   if ("yearly" %in% colnames(df)) {
@@ -1083,12 +1086,16 @@ plot_holidays <- function(m, df, uncertainty = TRUE) {
 #'
 #' @param m Prophet model object
 #' @param uncertainty Boolean to plot uncertainty intervals.
+#' @param weekly_start Integer specifying the start day of the weekly
+#'  seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day
+#'  to Monday, and so on.
 #'
 #' @return A ggplot2 plot.
-plot_weekly <- function(m, uncertainty = TRUE) {
+plot_weekly <- function(m, uncertainty = TRUE, weekly_start = 0) {
   # Compute weekly seasonality for a Sun-Sat sequence of dates.
   df.w <- data.frame(
-    ds=seq.Date(zoo::as.Date('2017-01-01'), by='d', length.out=7), cap=1.)
+    ds=seq.Date(zoo::as.Date('2017-01-01'), by='d', length.out=7) +
+    weekly_start, cap=1.)
   df.w <- setup_dataframe(m, df.w)$df
   seas <- predict_seasonal_components(m, df.w)
   seas$dow <- factor(weekdays(df.w$ds), levels=weekdays(df.w$ds))

R/man/plot_weekly.Rd

@@ -4,12 +4,16 @@
 \alias{plot_weekly}
 \title{Plot the weekly component of the forecast.}
 \usage{
-plot_weekly(m, uncertainty = TRUE)
+plot_weekly(m, uncertainty = TRUE, weekly_start = 0)
 }
 \arguments{
 \item{m}{Prophet model object}
 
 \item{uncertainty}{Boolean to plot uncertainty intervals.}
+
+\item{weekly_start}{Integer specifying the start day of the weekly
+seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day
+to Monday, and so on.}
 }
 \value{
 A ggplot2 plot.

R/man/prophet_plot_components.Rd

@@ -6,7 +6,8 @@
 Prints a ggplot2 with panels for trend, weekly and yearly seasonalities if
 present, and holidays if present.}
 \usage{
-prophet_plot_components(m, fcst, uncertainty = TRUE, plot_cap = TRUE)
+prophet_plot_components(m, fcst, uncertainty = TRUE, plot_cap = TRUE,
+  weekly_start = 0)
 }
 \arguments{
 \item{m}{Prophet object.}
@@ -18,6 +19,10 @@ plotted for the trend, from fcst columns trend_lower and trend_upper.}
 
 \item{plot_cap}{Boolean indicating if the capacity should be shown in the
 figure, if available.}
+
+\item{weekly_start}{Integer specifying the start day of the weekly
+seasonality plot. 0 (default) starts the week on Sunday. 1 shifts by 1 day
+to Monday, and so on.}
 }
 \value{
 Invisibly return a list containing the plotted ggplot objects

python/fbprophet/forecaster.py

@@ -914,7 +914,8 @@ class Prophet(object):
         fig.tight_layout()
         return fig
 
-    def plot_components(self, fcst, uncertainty=True, plot_cap=True):
+    def plot_components(self, fcst, uncertainty=True, plot_cap=True,
+                        weekly_start=0):
         """Plot the Prophet forecast components.
 
         Will plot whichever are available of: trend, holidays, weekly
@@ -926,6 +927,9 @@ class Prophet(object):
         uncertainty: Optional boolean to plot uncertainty intervals.
         plot_cap: Optional boolean indicating if the capacity should be shown
             in the figure, if available.
+        weekly_start: Optional int specifying the start day of the weekly
+            seasonality plot. 0 (default) starts the week on Sunday. 1 shifts
+            by 1 day to Monday, and so on.
 
         Returns
         -------
@@ -951,7 +955,8 @@ class Prophet(object):
                 artists += self.plot_holidays(fcst, ax=ax,
                                               uncertainty=uncertainty)
             elif plot == 'weekly':
-                artists += self.plot_weekly(ax=ax, uncertainty=uncertainty)
+                artists += self.plot_weekly(ax=ax, uncertainty=uncertainty,
+                                            weekly_start=weekly_start)
             elif plot == 'yearly':
                 artists += self.plot_yearly(ax=ax, uncertainty=uncertainty)
 
@@ -1027,7 +1032,7 @@ class Prophet(object):
         ax.set_ylabel('holidays')
         return artists
 
-    def plot_weekly(self, ax=None, uncertainty=True):
+    def plot_weekly(self, ax=None, uncertainty=True, weekly_start=0):
         """Plot the weekly component of the forecast.
 
         Parameters
@@ -1035,6 +1040,9 @@ class Prophet(object):
         ax: Optional matplotlib Axes to plot on. One will be created if this
             is not provided.
         uncertainty: Optional boolean to plot uncertainty intervals.
+        weekly_start: Optional int specifying the start day of the weekly
+            seasonality plot. 0 (default) starts the week on Sunday. 1 shifts
+            by 1 day to Monday, and so on.
 
         Returns
         -------
@@ -1045,7 +1053,8 @@ class Prophet(object):
             fig = plt.figure(facecolor='w', figsize=(10, 6))
             ax = fig.add_subplot(111)
         # Compute weekly seasonality for a Sun-Sat sequence of dates.
-        days = pd.date_range(start='2017-01-01', periods=7)
+        days = (pd.date_range(start='2017-01-01', periods=7) +
+                pd.Timedelta(days=weekly_start))
         df_w = pd.DataFrame({'ds': days, 'cap': 1.})
         df_w = self.setup_dataframe(df_w)
         seas = self.predict_seasonal_components(df_w)