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