hierarchicalforecast VS hts

I also recommend you check Nixtla's libraries, in particular StatsForecast and HierarchicalForecast. They offer a wide selection of forecasting models, and can work with multiple time series. Given that you're working with many products in a warehouse, I think the hierarchical forecast can be very useful, especially for the short time series (the ones that don't seem to have enough time stamps).

Repo: https://github.com/Nixtla/hierarchicalforecast Example: https://nixtla.github.io/hierarchicalforecast/examples/australiandomestictourism-intervals.html

If you use python take a look to the HierarchicalForecast package.

Here is the repo for hierarchical methods: https://github.com/nixtla/hierarchicalforecast/

You can try HierarchicalForecast package to reconciliate predictions.

[HierarchicalForecast package](https://github.com/Nixtla/hierarchicalforecast) that mirrors [hts](https://cran.r-project.org/web/packages/hts/vignettes/hts.pdf) that is now part of fable. The same with previous comment on efficient implementations of ARIMA and ETS on the [StatsForecast package](https://github.com/Nixtla/statsforecast).

sadly a lot of statistics research is done with R and is unavailable with Python, hopefully this kind of work will also motivate new libraries for Python. I am particularly interested in hierarchical forecasting. Are there Python alternatives to the hts library?(https://github.com/earowang/hts)

Additionally, you could use one of the more complex methods from the aforementioned hts package. This will allow you to make forecasts on all levels of the hierarchy, and use the bootstrapped errors to make adjustments to all forecasts in the hierarchy using a constrained least-squares approach, in order to make all forecasts sum-consistent (make the aggregates of the forecasts equal the forecasts of the aggregates). This allows you to model cannibalisation effects between different products, for example. However for this to work, you'd need quite good models, as the bootstrapped errors are taken as the 'wiggle room' for the adjustments, which means that if you have a badly fitting model, the adjustments might be quite large and no longer make sense (eg. be negative for a sales forecast).