TFTModel#

class TFTModel(decoder_length: int | None = None, encoder_length: int | None = None, dataset_builder: PytorchForecastingDatasetBuilder | None = None, train_batch_size: int = 64, test_batch_size: int = 64, lr: float = 0.001, hidden_size: int = 16, lstm_layers: int = 1, attention_head_size: int = 4, dropout: float = 0.1, hidden_continuous_size: int = 8, loss: MultiHorizonMetric | None = None, trainer_params: Dict[str, Any] | None = None, quantiles_kwargs: Dict[str, Any] | None = None, **kwargs)[source]#

Bases: _DeepCopyMixin, PytorchForecastingMixin, SaveNNMixin, PredictionIntervalContextRequiredAbstractModel

Wrapper for pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer.

Notes

We save pytorch_forecasting.data.timeseries.TimeSeriesDataSet in instance to use it in the model. It`s not right pattern of using Transforms and TSDataset.

Initialize TFT wrapper.

Parameters:

decoder_length (int | None) – Decoder length.
encoder_length (int) – Encoder length.
dataset_builder (etna.models.nn.utils.PytorchForecastingDatasetBuilder) – Dataset builder for PytorchForecasting.
train_batch_size (int) – Train batch size.
test_batch_size (int) – Test batch size.
lr (float) – Learning rate.
hidden_size (int) – Hidden size of network which can range from 8 to 512.
lstm_layers (int) – Number of LSTM layers.
attention_head_size (int) – Number of attention heads.
dropout (float) – Dropout rate.
hidden_continuous_size (int) – Hidden size for processing continuous variables.
loss (MultiHorizonMetric) – Loss function taking prediction and targets. Defaults to pytorch_forecasting.metrics.QuantileLoss.
trainer_kwargs – Additional arguments for pytorch_lightning Trainer.
quantiles_kwargs (Dict[str, Any] | None) – Additional arguments for computing quantiles, look at to_quantiles() method for your loss.
trainer_params (Dict[str, Any]) –

Methods

`fit`(ts)	Fit model.
`forecast`(ts, prediction_size[, ...])	Make predictions.
`get_model`()	Get internal model that is used inside etna class.
`load`(path)	Load an object.
`params_to_tune`()	Get default grid for tuning hyperparameters.
`predict`(ts, prediction_size[, ...])	Make predictions.
`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.
`trainer_params`
`dataset_builder`
`train_batch_size`
`test_batch_size`
`encoder_length`

fit(ts: TSDataset)[source]#

Fit model.

Parameters:: ts (TSDataset) – TSDataset to fit.
Returns:: model

forecast(ts: TSDataset, prediction_size: int, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975), return_components: bool = False) → TSDataset[source]#

Make predictions.

This method will make autoregressive predictions.

Parameters:

ts (TSDataset) – Dataset with features
prediction_size (int) – Number of last timestamps to leave after making prediction. Previous timestamps will be used as a context for models that require it.
prediction_interval (bool) – If True returns prediction interval for forecast
quantiles (Sequence[float]) – Levels of prediction distribution. By default 2.5% and 97.5% are taken to form a 95% prediction interval
return_components (bool) – If True additionally returns forecast components

Returns:

TSDataset with predictions.

Return type:

TSDataset

get_model() → Any[source]#

Get internal model that is used inside etna class.

Model is the instance of pytorch_forecasting.models.temporal_fusion_transformer.TemporalFusionTransformer.

Returns:: Internal model
Return type:: Any

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: hidden_size, lstm_layers, dropout, attention_head_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, prediction_interval: bool = False, quantiles: Sequence[float] = (0.025, 0.975), 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
prediction_size (int) – Number of last timestamps to leave after making prediction. Previous timestamps will be used as a context.
prediction_interval (bool) – If True returns prediction interval for forecast
quantiles (Sequence[float]) – Levels of prediction distribution. By default 2.5% and 97.5% are taken to form a 95% prediction interval
return_components (bool) – If True additionally returns prediction components

Returns:

TSDataset with predictions.

Return type:

TSDataset

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.