NBeatsInterpretableModel#

class NBeatsInterpretableModel(input_size: int, output_size: int, loss: Literal['mse'] | Literal['mae'] | Literal['smape'] | Literal['mape'] | Module = 'mse', trend_blocks: int = 3, trend_layers: int = 4, trend_layer_size: int = 256, degree_of_polynomial: int = 2, seasonality_blocks: int = 3, seasonality_layers: int = 4, seasonality_layer_size: int = 2048, num_of_harmonics: int = 1, lr: float = 0.001, window_sampling_limit: int | None = None, optimizer_params: dict | None = None, train_batch_size: int = 1024, test_batch_size: int = 1024, trainer_params: dict | None = None, train_dataloader_params: dict | None = None, test_dataloader_params: dict | None = None, val_dataloader_params: dict | None = None, split_params: dict | None = None, random_state: int | None = None)[source]#

Bases: NBeatsBaseModel

Interpretable N-BEATS model.

Paper: https://arxiv.org/pdf/1905.10437.pdf

Official implementation: ServiceNow/N-BEATS

Note

This model requires torch extension to be installed. Read more about this at installation page.

Init interpretable N-BEATS model.

Parameters:

input_size (int) – Input data size.
output_size (int) – Forecast size.
loss (Literal['mse'] | ~typing.Literal['mae'] | ~typing.Literal['smape'] | ~typing.Literal['mape'] | torch.nn.Module) – Optimisation objective. The loss function should accept three arguments: y_true, y_pred and mask. The last parameter is a binary mask that denotes which points are valid forecasts. There are several implemented loss functions available in the etna.models.nn.nbeats.metrics module.
trend_blocks (int) – Number of trend blocks.
trend_layers (int) – Number of inner layers in each trend block.
trend_layer_size (int) – Inner layer size in trend blocks.
degree_of_polynomial (int) – Polynomial degree for trend modeling.
seasonality_blocks (int) – Number of seasonality blocks.
seasonality_layers (int) – Number of inner layers in each seasonality block.
seasonality_layer_size (int) – Inner layer size in seasonality blocks.
num_of_harmonics (int) – Number of harmonics for seasonality estimation.
lr (float) – Optimizer learning rate.
window_sampling_limit (int | None) – Size of history for sampling training data. If set to None full series history used for sampling.
optimizer_params (dict | None) – Additional parameters for the optimizer.
train_batch_size (int) – Batch size for training.
test_batch_size (int) – Batch size for testing.
optimizer_params – Parameters for optimizer for Adam optimizer (api reference torch.optim.Adam).
trainer_params (dict | None) – Pytorch lightning trainer parameters (api reference pytorch_lightning.trainer.trainer.Trainer).
train_dataloader_params (dict | None) – Parameters for train dataloader like sampler for example (api reference torch.utils.data.DataLoader).
test_dataloader_params (dict | None) – Parameters for test dataloader.
val_dataloader_params (dict | None) – Parameters for validation dataloader.
split_params (dict | None) –
Dictionary with parameters for torch.utils.data.random_split() for train-test splitting
- train_size: (float) value from 0 to 1 - fraction of samples to use for training
- generator: (Optional[torch.Generator]) - generator for reproducibile train-test splitting
- torch_dataset_size: (Optional[int]) - number of samples in dataset, in case of dataset not implementing __len__
random_state (int | None) – Random state for train batches generation.

Methods

`fit`(ts)	Fit model.
`forecast`(ts, prediction_size[, ...])	Make predictions.
`get_model`()	Get model.
`load`(path)	Load an object.
`params_to_tune`()	Get default grid for tuning hyperparameters.
`predict`(ts, prediction_size[, return_components])	Make predictions.
`raw_fit`(torch_dataset)	Fit model on torch like Dataset.
`raw_predict`(torch_dataset)	Make inference on torch like Dataset.
`save`(path)	Save the object.
`set_params`(**params)	Return new object instance with modified parameters.
`to_dict`()	Collect all information about etna object in dict.

Attributes

This class stores its __init__ parameters as attributes.

context_size

Context size of the model.

fit(ts: TSDataset) → DeepBaseModel[source]#

Fit model.

Parameters:: ts (TSDataset) – TSDataset with features
Returns:: Model after fit
Return type:: DeepBaseModel

forecast(ts: TSDataset, prediction_size: int, return_components: bool = False) → TSDataset[source]#

Make predictions.

This method will make autoregressive predictions.

Parameters:

ts (TSDataset) – Dataset with features and expected decoder length for context
prediction_size (int) – Number of last timestamps to leave after making prediction. Previous timestamps will be used as a context.
return_components (bool) – If True additionally returns forecast components

Returns:

Dataset with predictions

Return type:

TSDataset

get_model() → DeepBaseNet[source]#

Get model.

Returns:: Torch Module
Return type:: DeepBaseNet

classmethod load(path: Path) → Self[source]#

Load an object.

Parameters:: path (Path) – Path to load object from.
Returns:: Loaded object.
Return type:: Self

params_to_tune() → Dict[str, BaseDistribution][source]#

Get default grid for tuning hyperparameters.

This grid tunes parameters: trend_blocks, trend_layers, trend_layer_size, degree_of_polynomial, seasonality_blocks, seasonality_layers, seasonality_layer_size, lr. Other parameters are expected to be set by the user.

Returns:: Grid to tune.
Return type:: Dict[str, BaseDistribution]

predict(ts: TSDataset, prediction_size: int, return_components: bool = False) → TSDataset[source]#

Make predictions.

This method will make predictions using true values instead of predicted on a previous step. It can be useful for making in-sample forecasts.

Parameters:

ts (TSDataset) – Dataset with features and expected decoder length for context
prediction_size (int) – Number of last timestamps to leave after making prediction. Previous timestamps will be used as a context.
return_components (bool) – If True additionally returns prediction components

Returns:

Dataset with predictions

Return type:

TSDataset

raw_fit(torch_dataset: Dataset) → DeepBaseModel[source]#

Fit model on torch like Dataset.

Parameters:: torch_dataset (Dataset) – Torch like dataset for model fit
Returns:: Model after fit
Return type:: DeepBaseModel

raw_predict(torch_dataset: Dataset) → Dict[Tuple[str, str], ndarray][source]#

Make inference on torch like Dataset.

Parameters:: torch_dataset (Dataset) – Torch like dataset for model inference
Returns:: Dictionary with predictions
Return type:: Dict[Tuple[str, str], ndarray]

save(path: Path)[source]#

Save the object.

Parameters:: path (Path) – Path to save object to.

set_params(**params: dict) → Self[source]#

Return new object instance with modified parameters.

Method also allows to change parameters of nested objects within the current object. For example, it is possible to change parameters of a model in a Pipeline.

Nested parameters are expected to be in a <component_1>.<...>.<parameter> form, where components are separated by a dot.

Parameters:: **params (dict) – Estimator parameters
Returns:: New instance with changed parameters
Return type:: Self

Examples

>>> from etna.pipeline import Pipeline
>>> from etna.models import NaiveModel
>>> from etna.transforms import AddConstTransform
>>> model = model=NaiveModel(lag=1)
>>> transforms = [AddConstTransform(in_column="target", value=1)]
>>> pipeline = Pipeline(model, transforms=transforms, horizon=3)
>>> pipeline.set_params(**{"model.lag": 3, "transforms.0.value": 2})
Pipeline(model = NaiveModel(lag = 3, ), transforms = [AddConstTransform(in_column = 'target', value = 2, inplace = True, out_column = None, )], horizon = 3, )

to_dict()[source]#: Collect all information about etna object in dict.

property context_size: int[source]#: Context size of the model.