LTSFLinearForecaster

class LTSFLinearForecaster(seq_len, pred_len, *, num_epochs=16, batch_size=8, in_channels=1, individual=False, criterion=None, criterion_kwargs=None, optimizer=None, optimizer_kwargs=None, lr=0.001, custom_dataset_train=None, custom_dataset_pred=None)[source]

LTSF-Linear Forecaster.

Implementation of the Long-Term Short-Term Feature (LTSF) linear forecaster, aka LTSF-Linear, by Zeng et al [1].

Core logic is directly copied from the cure-lab LTSF-Linear implementation [Rdf9423af0e01-2], which is unfortunately not available as a package.

Parameters:

seq_lenint: length of input sequence
pred_lenint: length of prediction (forecast horizon)
num_epochsint, default=16: number of epochs to train
batch_sizeint, default=8: number of training examples per batch
in_channelsint, default=1: number of input channels passed to network
individualbool, default=False: boolean flag that controls whether the network treats each channel individually” “or applies a single linear layer across all channels. If individual=True, the” “a separate linear layer is created for each input channel. If” “individual=False, a single shared linear layer is used for all channels.”
criteriontorch.nn Loss Function, default=torch.nn.MSELoss: loss function to be used for training
criterion_kwargsdict, default=None: keyword arguments to pass to criterion
optimizertorch.optim.Optimizer, default=torch.optim.Adam: optimizer to be used for training
optimizer_kwargsdict, default=None: keyword arguments to pass to optimizer
lrfloat, default=0.003: learning rate to train model with

Attributes:

cutoff: Cut-off = “present time” state of forecaster.
fh: Forecasting horizon that was passed.
is_fitted: Whether fit has been called.
state: State of the estimator.

References

[1]

Zeng A, Chen M, Zhang L, Xu Q. 2023.

Are transformers effective for time series forecasting? Proceedings of the AAAI conference on artificial intelligence 2023 (Vol. 37, No. 9, pp. 11121-11128). .. [Rdf9423af0e01-2] https://github.com/cure-lab/LTSF-Linear

Examples

>>> from sktime.forecasting.ltsf import LTSFLinearForecaster
>>> from sktime.datasets import load_airline
>>> model = LTSFLinearForecaster(10, 3)
>>> y = load_airline()
>>> model.fit(y, fh=[1,2,3])
LTSFLinearForecaster(pred_len=3, seq_len=10)
>>> y_pred = model.predict()
>>> y_pred
1961-01    515.456726
1961-02    576.704712
1961-03    559.859680
Freq: M, Name: Number of airline passengers, dtype: float32

Methods

`build_pytorch_pred_dataloader`(y, fh)	Build PyTorch DataLoader for prediction.
`build_pytorch_train_dataloader`(y)	Build PyTorch DataLoader for training.
`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(y[, X, fh])	Fit forecaster to training data.
`fit_predict`(y[, X, fh, X_pred])	Fit and forecast time series at future horizon.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_pretrained_params`([deep])	Get pretrained parameters of this estimator.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`get_y_true`(y)	Get y_true values for validation.
`is_composite`()	Check if the object is composed of other BaseObjects.
`load_from_path`(serial)	Load object from file location.
`load_from_serial`(serial)	Load object from serialized memory container.
`predict`([fh, X])	Forecast time series at future horizon.
`predict_interval`([fh, X, coverage])	Compute/return prediction interval forecasts.
`predict_proba`([fh, X, marginal])	Compute/return fully probabilistic forecasts.
`predict_quantiles`([fh, X, alpha])	Compute/return quantile forecasts.
`predict_residuals`([y, X])	Return residuals of time series forecasts.
`predict_var`([fh, X, cov])	Compute/return variance forecasts.
`pretrain`(y[, X, fh])	Pre-train forecaster on panel (global) data.
`reset`()	Reset the object to a clean post-init state.
`save`([path, serialization_format])	Save serialized self to bytes-like object or to (.zip) file.
`score`(y[, X, fh])	Scores forecast against ground truth, using MAPE (non-symmetric).
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(y[, X, update_params])	Update cutoff value and, optionally, fitted parameters.
`update_predict`(y[, cv, X, update_params, ...])	Make predictions and update model iteratively over the test set.
`update_predict_single`([y, fh, X, update_params])	Update model with new data and make forecasts.