1. Auxiliary functions
1.1 WaveKAN
source
WaveKANLayer
*This is a sample code for the simulations of the paper: Bozorgasl, Zavareh and Chen, Hao, Wav-KAN: Wavelet Kolmogorov-Arnold Networks (May, 2024) https://arxiv.org/abs/2405.12832 and also available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4835325 We used efficient KAN notation and some part of the code:+*
1.2 TaylorKAN
source
TaylorKANLayer
https://github.com/Muyuzhierchengse/TaylorKAN/
1.3. JacobiKAN
source
JacobiKANLayer
https://github.com/SpaceLearner/JacobiKAN/blob/main/JacobiKANLayer.py
2. Model
source
RMoK
*Reversible Mixture of KAN Parameters:
h: int, Forecast horizon. input_size: int,
autorregresive inputs size, y=[1,2,3,4] input_size=2 ->
y_[t-2:t]=[1,2].n_series: int, number of time-series.futr_exog_list: str list, future exogenous columns.hist_exog_list: str list, historic exogenous columns.stat_exog_list: str list, static exogenous columns.taylor_order: int, order of the Taylor polynomial.jacobi_degree: int, degree of the Jacobi polynomial.wavelet_function: str, wavelet function to use in the WaveKAN. Choose
from [“mexican_hat”, “morlet”, “dog”, “meyer”, “shannon”]dropout: float, dropout rate.revin_affine: bool=False, bool to
use affine in RevIn.loss: PyTorch module, instantiated train loss
class from losses
collection.valid_loss: PyTorch module=loss, instantiated valid loss class from
losses
collection.max_steps: int=1000, maximum number of training steps.learning_rate: float=1e-3, Learning rate between (0, 1).num_lr_decays: int=-1, Number of learning rate decays, evenly
distributed across max_steps.early_stop_patience_steps: int=-1,
Number of validation iterations before early stopping.val_check_steps: int=100, Number of training steps between every
validation loss check.batch_size: int=32, number of different
series in each batch.valid_batch_size: int=None, number of
different series in each validation and test batch, if None uses
batch_size.windows_batch_size: int=32, number of windows to
sample in each training batch, default uses all.inference_windows_batch_size: int=32, number of windows to sample in
each inference batch, -1 uses all.start_padding_enabled:
bool=False, if True, the model will pad the time series with zeros at
the beginning, by input size.step_size: int=1, step size between
each window of temporal data.scaler_type: str=‘identity’, type of
scaler for temporal inputs normalization see temporal
scalers.random_seed: int=1, random_seed for pytorch initializer and numpy
generators.drop_last_loader: bool=False, if True
TimeSeriesDataLoader drops last non-full batch.alias: str,
optional, Custom name of the model.optimizer: Subclass of
‘torch.optim.Optimizer’, optional, user specified optimizer instead of
the default choice (Adam).optimizer_kwargs: dict, optional, list
of parameters used by the user specified optimizer.lr_scheduler: Subclass of ‘torch.optim.lr_scheduler.LRScheduler’,
optional, user specified lr_scheduler instead of the default choice
(StepLR).lr_scheduler_kwargs: dict, optional, list of parameters
used by the user specified lr_scheduler.dataloader_kwargs:
dict, optional, list of parameters passed into the PyTorch Lightning
dataloader by the TimeSeriesDataLoader. **trainer_kwargs: int,
keyword trainer arguments inherited from PyTorch Lighning’s
trainer.References
- Xiao Han, Xinfeng Zhang, Yiling Wu, Zhenduo Zhang, Zhe Wu.”KAN4TSF: Are KAN and KAN-based models Effective for Time Series Forecasting?“. arXiv.
*
RMoK.fit
*Fit. The
fit method, optimizes the neural network’s weights using the
initialization parameters (learning_rate, windows_batch_size, …) and
the loss function as defined during the initialization. Within fit
we use a PyTorch Lightning Trainer that inherits the initialization’s
self.trainer_kwargs, to customize its inputs, see PL’s trainer
arguments.
The method is designed to be compatible with SKLearn-like classes and in
particular to be compatible with the StatsForecast library.
By default the model is not saving training checkpoints to protect
disk memory, to get them change enable_checkpointing=True in
__init__.
Parameters:dataset: NeuralForecast’s
TimeSeriesDataset,
see
documentation.val_size: int, validation size for temporal cross-validation.random_seed: int=None, random_seed for pytorch initializer and numpy
generators, overwrites model.__init__’s.test_size: int, test
size for temporal cross-validation.*
RMoK.predict
*Predict. Neural network prediction with PL’s
Trainer execution of
predict_step.
Parameters:dataset: NeuralForecast’s
TimeSeriesDataset,
see
documentation.test_size: int=None, test size for temporal cross-validation.step_size: int=1, Step size between each window.random_seed:
int=None, random_seed for pytorch initializer and numpy generators,
overwrites model.__init__’s.quantiles: list of floats,
optional (default=None), target quantiles to predict. **data_module_kwargs: PL’s TimeSeriesDataModule args, see
documentation.*