marah othman
07/12/2023, 11:39 AMCristian (Nixtla)
07/12/2023, 2:26 PMmarah othman
07/12/2023, 11:31 PMCristian (Nixtla)
07/14/2023, 2:06 PMmarah othman
07/15/2023, 12:07 PMCristian (Nixtla)
07/15/2023, 12:08 PMmarah othman
07/15/2023, 12:10 PMCristian (Nixtla)
07/15/2023, 12:12 PMinput_size_multiplier
and change it to input_size
directly. You could have if conditions before defining the config dictionarymarah othman
07/15/2023, 12:12 PMCristian (Nixtla)
07/15/2023, 12:21 PMmarah othman
07/15/2023, 12:23 PMCristian (Nixtla)
07/15/2023, 12:23 PMBaseWindows
and BaseRecurrent
base classes, in the create_windows
methodmarah othman
07/15/2023, 12:24 PMCristian (Nixtla)
07/15/2023, 1:27 PMauto
part to select hyperparameters. Windows-based models are functions from past values (input_size
) to future values (horizon
). During training, models sample a batch of windows (of size input_size+h
) starting at random timestamps. They produce the forecasts for all windows of the batch, compute training loss (based on the last h
points of each window), and update parameters.auto
models automatize the pipeline of trying different hyperparameters, observing their performance in a validation set, and selecting the best. They train num_samples
different models with different hyperparameters, evaluate them on the validation set, and store the best based on the validation loss.marah othman
07/15/2023, 3:17 PMCristian (Nixtla)
07/15/2023, 4:04 PMval_size
parameter of the fit
and cross_validation
methods. If you dont specify val_size
, auto models will use a validation set equal to 1 horizon of length. The test set is specified by the user, and it is only used for the cross_validation
function. The models is NOT automatically evaluated in the test set, instead, the function will return the forecasts for the test set so you can visualize/evaluate them.marah othman
07/15/2023, 11:56 PM