The standard methods include data preprocessing _normalization, optimization utilities like parameter initialization, training_step, validation_step, and shared fit and predict methods.These shared methods enable all the neuralforecast.models compatibility with the core.NeuralForecast wrapper class.

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BaseWindows

 BaseWindows (h, input_size, loss, valid_loss, learning_rate, max_steps,
              val_check_steps, batch_size, valid_batch_size,
              windows_batch_size, inference_windows_batch_size,
              start_padding_enabled, step_size=1, num_lr_decays=0,
              early_stop_patience_steps=-1, scaler_type='identity',
              futr_exog_list=None, hist_exog_list=None,
              stat_exog_list=None, exclude_insample_y=False,
              num_workers_loader=0, drop_last_loader=False, random_seed=1,
              alias=None, optimizer=None, optimizer_kwargs=None,
              lr_scheduler=None, lr_scheduler_kwargs=None,
              dataloader_kwargs=None, **trainer_kwargs)

*Base Windows

Base class for all windows-based models. The forecasts are produced separately for each window, which are randomly sampled during training.

This class implements the basic functionality for all windows-based models, including: - PyTorch Lightning’s methods training_step, validation_step, predict_step.
- fit and predict methods used by NeuralForecast.core class.
- sampling and wrangling methods to generate windows.*

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BaseWindows.fit

 BaseWindows.fit (dataset, val_size=0, test_size=0, random_seed=None,
                  distributed_config=None)

*Fit.

The fit method, optimizes the neural network’s weights using the initialization parameters (learning_rate, windows_batch_size, …) and the loss function as defined during the initialization. Within fit we use a PyTorch Lightning Trainer that inherits the initialization’s self.trainer_kwargs, to customize its inputs, see PL’s trainer arguments.

The method is designed to be compatible with SKLearn-like classes and in particular to be compatible with the StatsForecast library.

By default the model is not saving training checkpoints to protect disk memory, to get them change enable_checkpointing=True in __init__.

Parameters:
dataset: NeuralForecast’s TimeSeriesDataset, see documentation.
val_size: int, validation size for temporal cross-validation.
random_seed: int=None, random_seed for pytorch initializer and numpy generators, overwrites model.__init__’s.
test_size: int, test size for temporal cross-validation.
*

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BaseWindows.predict

 BaseWindows.predict (dataset, test_size=None, step_size=1,
                      random_seed=None, **data_module_kwargs)

*Predict.

Neural network prediction with PL’s Trainer execution of predict_step.

Parameters:
dataset: NeuralForecast’s TimeSeriesDataset, see documentation.
test_size: int=None, test size for temporal cross-validation.
step_size: int=1, Step size between each window.
random_seed: int=None, random_seed for pytorch initializer and numpy generators, overwrites model.__init__’s.
**data_module_kwargs: PL’s TimeSeriesDataModule args, see documentation.*

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BaseWindows.decompose

 BaseWindows.decompose (dataset, step_size=1, random_seed=None,
                        **data_module_kwargs)

*Decompose Predictions.

Decompose the predictions through the network’s layers. Available methods are ESRNN, NHITS, NBEATS, and NBEATSx.

Parameters:
dataset: NeuralForecast’s TimeSeriesDataset, see documentation here.
step_size: int=1, step size between each window of temporal data.
**data_module_kwargs: PL’s TimeSeriesDataModule args, see documentation.*

BaseRecurrentTemporalNorm