ESRNNForecaster

class ESRNNForecaster(hidden_size=10, num_layer=5, season1_length=12, season2_length=6, seasonality_type='single', window=10, pred_len=3, stride=1, batch_size=32, num_epochs=1000, criterion=None, optimizer='Adam', lr=0.1, optimizer_kwargs=None, criterion_kwargs=None, custom_dataset_train=None, custom_dataset_pred=None)[source]

Exponential Smoothing Recurrant Neural Network.

This model combines Exponential Smoothing (ES) and (LSTM) networks for time series forecasting. ES is used to balance the level and seasonality of the series. This method has been proposed in [1].

Parameters:

hidden_sizeint: Number of features in the hidden state
num_layerint: Number of layers
seasonality_typestring: Type of seasonality_type, could be zero ,single or double
season1_lengthint: Period of season 1
season2_lengthint: Period of season 2
strideint: stride for sliding window
batch_sizeint: size of batch during training
num_epochsint: number of epochs during training
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
windowint: Size of Input window, default=10
pred_lenint: Prediction length, i.e., the number of future time steps to forecast. Defines the network output dimension, default=3
lrint: Learning rate for training

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]

Smyl, S. 2020.

A hybrid method of exponential smoothing and recurrent neural networks for time series forecasting. https://www.sciencedirect.com/science/article/pii/S0169207019301153

Examples

>>> from sktime.forecasting.es_rnn import ESRNNForecaster
>>> from sktime.datasets import load_airline
>>> from sktime.transformations.boxcox import LogTransformer
>>> y = load_airline()
>>> scaler=LogTransformer()
>>> forecaster=ESRNNForecaster(15,6,12,6,'double',20,1,32,100,'MSE')
>>> y_new=scaler.fit_transform(y)
>>> forecaster.fit(y_new, fh=[1,2,3])
>>> y_pred = forecaster.predict()
>>> y_pred=scaler.inverse_transform(y_pred)

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.