ESRNNForecaster

Schnellstart

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)

Parameter(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

Beispiele

>>> 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)

Referenzen