CINNForecaster

Conditional Invertible Neural Network (cINN) Forecaster.

Quickstart

from sktime.forecasting.cinn import CINNForecaster

estimator = CINNForecaster(n_coupling_layers=10, hidden_dim_size=32, sample_dim=24, batch_size=64, encoded_cond_size=64, lr=0.0005, weight_decay=1e-05, sp_list=None, fourier_terms_list=None, window_size=720, num_epochs=50, verbose=False, f_statistic=None, init_param_f_statistic=None, deterministic=False, lag_feature='mean', patience=5, delta=0.0001, val_split=0.2)

Parameters(19)

n_coupling_layersint, optional (default=15)

Number of coupling layers in the cINN.

hidden_dim_sizeint, optional (default=64)

Number of hidden units in the subnet.

sample_dimint, optional (default=24)

Dimension of the samples that the cINN is creating

batch_sizeint, optional (default=64)

Batch size for the training.

encoded_cond_sizeint, optional (default=64)

Dimension of the encoded condition.

lrfloat, optional (default=5e-4)

Learning rate for the Adam optimizer.

weight_decayfloat, optional (default=1e-5)

Weight decay for the Adam optimizer.

sp_listlist of int, optional (default=[24])

List of seasonal periods to use for the Fourier features.

fourier_terms_listlist of int, optional (default=[1, 1])

List of number of Fourier terms to use for the Fourier features.

window_sizeint, optional (default=24*30)

Window size for calculating the rolling statistics using the WindowSummarizer.

lag_feature: str, optional (default=”mean”)

The rolling statistic that the WindowSummarizer should calculate.

num_epochsint, optional (default=50)

Number of epochs to train the cINN.

verbosebool, optional (default=False)

Whether to print the training progress.

f_statisticfunction, optional (default=default_sine)

Function to use for forecasting the rolling statistic.

init_param_f_statisticlist of float, optional (default=[1, 0, 0, 10, 1, 1])

Initial parameters for the f_statistic function.

deterministicbool, optional (default=False)

Whether to use a deterministic or stochastic cINN. Note, deterministic should only used for testing.

patienceint, optional (default=5)

Number of epochs to wait before stopping the training.

deltafloat, optional (default=0.0001)

Minimum change in the validation loss to consider as an improvement.

val_splitfloat, optional (default=0.2)

Fraction of the data to use for validation.

Examples

>>> from sktime.forecasting.cinn import CINNForecaster
>>> from sktime.datasets import load_airline
>>> y = load_airline ()
>>> model = CINNForecaster (window_size = 100)
>>> model. fit (y) CINNForecaster(
... )
>>> y_pred = model. predict (fh = [1, 2, 3 ])

References

1. ..[1] Heidrich, B., Hertel, M., Neumann, O., Hagenmeyer, V., & Mikut, R. (2023). Using conditional Invertible Neural Networks to Perform Mid- Term Peak Load Forecasting. IET Smart Grid, Under Review