Background provided by @Chris Gervais:
Background:
• we have a feature store of ~5M features from power markets
• some of those are forecast features, others are actuals
• we can target any actuals series, and combine with future regressors (forecast features) and lagged regressors (actuals features)
Findings:
• when we generate datasets with mostly lagged features, TCN vs NHITS accuracy wise isn't that noticeable
• when we have lots of future regressors in the dataset, TCN gets a noticeable accuracy pickup, still need to quantify how much but early estimates are ~15-20% MSE improvement, will calculate RMSSE shortly for target-wide evaluation
• TCN trains considerably faster for the same dataset and target