Tamer A. Abdelmigid

I would like to second this. I had a case where improved forecasting performance was achieved by preprocessing the data using EMD and its variants. The case was a univariate...

@colin99d try ``` pl_trainer_kwargs={ "enable_model_summary": False, }, ```

@colin99d I'm currently using this: ``` warnings.filterwarnings("ignore") logging.basicConfig(level=logging.CRITICAL) logging.getLogger("darts.models").setLevel(logging.CRITICAL) logging.getLogger("pytorch_lightning").setLevel(logging.CRITICAL) logging.getLogger("darts.models.forecasting.pl_forecasting_module").setLevel(logging.CRITICAL) logging.getLogger("darts.models.forecasting.tcn_model").setLevel(logging.CRITICAL) logging.getLogger("darts.models.forecasting.nbeats").setLevel(logging.CRITICAL) logging.getLogger("darts.models.forecasting.NHiTSModel").setLevel(logging.CRITICAL) logging.getLogger("darts.models.forecasting.nhits").setLevel(logging.CRITICAL) logging.getLogger("darts.models.forecasting.torch_forecasting_model").setLevel(logging.CRITICAL) logging.getLogger("darts.models.forecasting.forecasting_model").setLevel(logging.CRITICAL) logging.getLogger("darts.models.forecasting.baselines").setLevel(logging.CRITICAL) logging.getLogger("pytorch_lightning.accelerators.gpu").setLevel(logging.CRITICAL) logging.getLogger("darts.timeseries").setLevel(logging.CRITICAL) logging.getLogger("darts.utils.utils").setLevel(logging.CRITICAL) logging.getLogger("darts.utils.torch").setLevel(logging.CRITICAL) logging.getLogger("darts.models.forecasting.tft_model").setLevel(logging.CRITICAL) logging.getLogger("darts.models.forecasting.tft_submodels").setLevel(logging.CRITICAL) logging.getLogger("darts.utils.data.training_dataset").setLevel(logging.CRITICAL) logging.getLogger("darts.utils.data.horizon_based_dataset").setLevel(logging.CRITICAL) logging.getLogger("darts.utils.statistics").setLevel(logging.CRITICAL) logging.getLogger("darts.dataprocessing.transformers.scaler").setLevel(logging.CRITICAL) logging.getLogger("darts.dataprocessing.transformers.fittable_data_transformer").setLevel(logging.CRITICAL) ```...

Error persist in Darts Version 0.21 The problem exists with historical_forecasts() it gives an error `point (int) should be a valid index in series` the problem is encountered regardless of...

Hey @hrzn, sadly that's not the case here. Here is a minimum code: ``` import numpy as np import pandas as pd import matplotlib.pyplot as plt from darts import TimeSeries...

@hrzn Thanks a lot for your efforts.

It would be super helpful if you could include an example on using PopulationBasedTraining of ray tune to tune a learning rate schedule of DARTS Torch models.