Marco Capuccini

I'll fix the CI if you're willing to merge. It seems like Travis fails because it can't push.

This should fix it: https://github.com/src-d/coreos-nvidia/pull/11.

Hi Gianluca, thanks for the quick answer. The threshold (sig. level) would remain the same but the p-value would be computed for each class separately according to the calibration examples...

I see your point. With a windowed scorer though one could match the size of the window with the lookback plus the forecasting horizion. As I understand k-means scorer considers...

@dennisbader I am trying out what you suggested but concatenating the historical forecast is also painfully slow: ```python def concatenate_predictions( historical_forecasts: List[List[TimeSeries]], ) -> List[TimeSeries]: # Figure out tot iterations...

Cool! I assume that the results from historical_forecasts are contiguous in time. You meant trying something like this right? ```python historical_forecasts = model.historical_forecasts( series=train_target_series, past_covariates=train_past_covariates, future_covariates=train_future_covariates, forecast_horizon=10, stride=10, last_points_only=False, retrain=False,...

Yes, it did :)