Does Neuralforecast provide something similar to Neuralprophet's Global-Local model where a global model can be used along with local models for some components such as trend and seasonality (https://neuralprophet.com/how-to-guides/feature-guides/global_local_modeling.html)? Thanks

Hey Manuel, None of our methods have this global-local model behavior. But I don't believe that neural-prophet architecture is a good reference for accurate predictions. https://www.reddit.com/r/MachineLearning/comments/wqrw8x/d_fool_me_once_shame_on_you_fool_me_twice_shame/

Hi @Kin Gtz. Olivares! I have yet to try it but this Local-Global model idea might be interesting. At the moment I am trying hierarchical forecasting with neuralforecast and hierarchicalforecast but I can't get good results. So far the best combination I have found is this: 1) I forecast the time series at the highest level of the hierarchy with ARIMA. 2) I forecast all time series using NHITS with loss SMAPE. 3) I replace the forecasts made by NHITS for the highest level of the hierarchy with those made using ARIMA (basically I make a forecast mix where the highest level of the hierarchy uses ARIMA and the lowest level of the hierarchy uses NHITS). 4) I perform a Top-Down reconciliation using hierarchicalforecast. I also tried HINT but probabilistic losses (GMM, NBMM, PMM...) do not perform well for me. Considering the mix of local (ARIMA) and global (NHITS) models I use now, NeuralProphet's Local-Global model looked interesting.

Top Down reconciliation with ARIMA learns proportions to disaggregate ARIMA into bottom level series. Depending on the version that you use 'average_proportions', 'proportion_averages' or 'forecasted_proportions', you could skip the NHITS model for the bottom.

@Kin Gtz. Olivares I need a global model at lower levels because some time series at lower levels of the hierarchy have a very short history and NHITS allows me to perform some kind of transfer learning to forecast short time series. Scheduling a call would be super nice!

Sounds good