I am trying to learning pyro to build a hierarchical time series forecasting model .
My target: predict
product_in_different_store for future 14 days salecount .
For example ,
- I have 4 levels hierarchy structure
category 1/ category 2 / product_in_different_city/ product_in_different_store.
- The exogenous features are bound to bottom level
product_in_different_storeand middle level
product_in_different_storemay have different distribution : normal , poisson , negative-binomial , zero-inflated-poisson or zero-inflated-negative-binomial
- Train ARIMA/Prophet on each
product_in_different_city with one-hot encode store featuresdoesn’t perform well , because each single product timeseries is lack of exogenous features , some features may be only happend once(but may apreared many times in different product) , hard to estimate best coefficient . So I am looking into hierarchy model .
- I can’t look so many product ( 4 cities , avg 30+ stores each city, about 8000+ product each store ) , seems many product only on selling in very short time ( less than 28 days , have blank period or stock out period ) . It is hard to choose one distribution for all of them .
- Could we choose distribution dynamicly in trainning ?
- Could you provide an example for hierarchical multivariate time series ? I have saw some hierarchy model only bind features to top level , how do I bound different feature to different levels ?