Forecast Stability Guidance for Model Selection

[This article was first published on Coastal Econometrician Views, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

In real world forecasting task, we don’t have luxury of actuals in hand for better model selection, in such realistic situations, forecast stability can guide us to some extent. Forecast Stability in simple terms, is all about how forecasts behave versus forecasts, we can measure it with simple coefficient of variation. This measure also helps us to understand non-randomness across the data. When we have data at SKU (Store Keep Unit) Level, looking at it regularly provides some extra information that can be used for correcting non-randomness, especially for low volume SKUs. In this notebook exercise, I have three consecutive weeks data for 50 SKUs actuals and presented forecasts, to demonstrate what we can deduce from regular observation of forecast stability with said simple measure.

PS: Demonstration is based on weekly model forecasts, which are either different or same models across weeks based on a selection procedure. Current demonstration selects models based on minimal error across the different time series models namely., ARIMA, SARIMA and ETS based on R package “forecast”.

Below link has the data and R notebook: 

 Happy R Programming!

To leave a comment for the author, please follow the link and comment on their blog: Coastal Econometrician Views. offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)