*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:+*
https://github.com/Muyuzhierchengse/TaylorKAN/
https://github.com/SpaceLearner/JacobiKAN/blob/main/JacobiKANLayer.py
*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.*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.*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.*