Predicting Intermittent Demand

Bruno Rodrigues shows one technique for forecasting intermittent data:

Now, it is clear that this will be tricky to forecast. There is no discernible pattern, no trend, no seasonality… nothing that would make it “easy” for a model to learn how to forecast such data.

This is typical intermittent demand data. Specific methods have been developed to forecast such data, the most well-known being Croston, as detailed in this paper. A function to estimate such models is available in the {tsintermittent} package, written by Nikolaos Kourentzes who also wrote another package, {nnfor}, which uses Neural Networks to forecast time series data. I am going to use both to try to forecast the intermittent demand for the {RDieHarder} package for the year 2019.

Read the whole thing. H/T R-Bloggers

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