Forecaster
ESRNNForecaster
Exponential Smoothing Recurrant Neural Network.
Quickstart
python
from sktime.forecasting.es_rnn import ESRNNForecaster
estimator = 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)Parameters(15)
- 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
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)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