Update vignettes/Common_Patterns.Rmd

Co-authored-by: Michael Mahoney <[email protected]>

vignettes/Common_Patterns.Rmd

@@ -222,4 +222,13 @@ And if you want to set the size of windows based on units of time, for instance
 sliding_period(Chicago, date, "year") %>%
   head(2)
 ```
+All of these functions produce analysis sets of the same size, with the start and end of the analysis set "sliding" down your data frame. If you'd rather have your analysis set get progressively larger, so that you're predicting new data based upon a growing set of older observations, you can use the `sliding_window()` function with `lookback = -Inf`:
 
+```{r}
+sliding_window(Chicago, lookback = Inf) %>%
+  head(2)
+```
+
+This is commonly referred to as "evaluation on a rolling forecasting origin", or more colloquially, "rolling origin cross-validation".
+
+Note that all of these time-based resampling functions are deterministic: unlike the rest of the package, running these functions repeatedly under different random seeds will always return the same results.