LTSFDLinearForecaster

Schnellstart

from sktime.forecasting.ltsf import LTSFDLinearForecaster

estimator = LTSFDLinearForecaster(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)

Parameter(11)

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

Beispiele

>>> from sktime.forecasting.ltsf import LTSFDLinearForecaster
>>> from sktime.datasets import load_airline
>>> model = LTSFDLinearForecaster (10, 3)
>>> y = load_airline ()
>>> model. fit (y, fh = [1, 2, 3 ]) LTSFDLinearForecaster(pred_len=3, seq_len=10)
>>> y_pred = model. predict ()
>>> y_pred 1961-01 436.494476 1961-02 433.659851 1961-03 479.309631 Freq: M, Name: Number of airline passengers, dtype: float32

Referenzen