> ## Documentation Index
> Fetch the complete documentation index at: https://nixtla-old-docs.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Reversible Mixture of KAN - RMoK
Figure 1. Architecture of
RMoK.
## 1. Auxiliary functions
### 1.1 WaveKAN
***
source
### WaveKANLayer
> ```text theme={null}
> WaveKANLayer (in_features, out_features, wavelet_type='mexican_hat',
> with_bn=True, device='cpu')
> ```
\*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](https://arxiv.org/abs/2405.12832) and also available at:
[https://papers.ssrn.com/sol3/papers.cfm?abstract\_id=4835325](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
> ```text theme={null}
> TaylorKANLayer (input_dim, out_dim, order, addbias=True)
> ```
*[https://github.com/Muyuzhierchengse/TaylorKAN/](https://github.com/Muyuzhierchengse/TaylorKAN/)*
### 1.3. JacobiKAN
***
source
### JacobiKANLayer
> ```text theme={null}
> JacobiKANLayer (input_dim, output_dim, degree, a=1.0, b=1.0)
> ```
*[https://github.com/SpaceLearner/JacobiKAN/blob/main/JacobiKANLayer.py](https://github.com/SpaceLearner/JacobiKAN/blob/main/JacobiKANLayer.py)*
## 2. Model
***
source
### RMoK
> ```text theme={null}
> RMoK (h, input_size, n_series:int, futr_exog_list=None,
> hist_exog_list=None, stat_exog_list=None, taylor_order:int=3,
> jacobi_degree:int=6, wavelet_function:str='mexican_hat',
> dropout:float=0.1, revin_affine:bool=True, loss=MAE(),
> valid_loss=None, max_steps:int=1000, learning_rate:float=0.001,
> num_lr_decays:int=-1, early_stop_patience_steps:int=-1,
> val_check_steps:int=100, batch_size:int=32,
> valid_batch_size:Optional[int]=None, windows_batch_size=32,
> inference_windows_batch_size=32, start_padding_enabled=False,
> step_size:int=1, scaler_type:str='identity', random_seed:int=1,
> drop_last_loader:bool=False, alias:Optional[str]=None,
> optimizer=None, optimizer_kwargs=None, lr_scheduler=None,
> lr_scheduler_kwargs=None, dataloader_kwargs=None, **trainer_kwargs)
> ```
\*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](https://nixtla.github.io/neuralforecast/losses.pytorch.html).
`valid_loss`: PyTorch module=`loss`, instantiated valid loss class from
[losses
collection](https://nixtla.github.io/neuralforecast/losses.pytorch.html).
`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](https://nixtla.github.io/neuralforecast/common.scalers.html).
`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](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=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.](https://arxiv.org/abs/2408.11306) \*
***
### RMoK.fit
> ```text theme={null}
> RMoK.fit (dataset, val_size=0, test_size=0, random_seed=None,
> distributed_config=None)
> ```
\*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](https://pytorch-lightning.readthedocs.io/en/stable/api/pytorch_lightning.trainer.trainer.Trainer.html?highlight=trainer).
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`](https://nixtlaverse.nixtla.io/neuralforecast/tsdataset.html#timeseriesdataset),
see
[documentation](https://nixtla.github.io/neuralforecast/tsdataset.html).
`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
> ```text theme={null}
> RMoK.predict (dataset, test_size=None, step_size=1, random_seed=None,
> quantiles=None, **data_module_kwargs)
> ```
\*Predict.
Neural network prediction with PL’s `Trainer` execution of
`predict_step`.
**Parameters:** `dataset`: NeuralForecast’s
[`TimeSeriesDataset`](https://nixtlaverse.nixtla.io/neuralforecast/tsdataset.html#timeseriesdataset),
see
[documentation](https://nixtla.github.io/neuralforecast/tsdataset.html).
`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](https://pytorch-lightning.readthedocs.io/en/1.6.1/extensions/datamodules.html#using-a-datamodule).\*
## 3. Usage example
```python theme={null}
import pandas as pd
import matplotlib.pyplot as plt
from neuralforecast import NeuralForecast
from neuralforecast.models import RMoK
from neuralforecast.utils import AirPassengersPanel, AirPassengersStatic
from neuralforecast.losses.pytorch import MSE
Y_train_df = AirPassengersPanel[AirPassengersPanel.ds=AirPassengersPanel['ds'].values[-12]].reset_index(drop=True) # 12 test
model = RMoK(h=12,
input_size=24,
n_series=2,
taylor_order=3,
jacobi_degree=6,
wavelet_function='mexican_hat',
dropout=0.1,
revin_affine=True,
loss=MSE(),
valid_loss=MAE(),
early_stop_patience_steps=3,
batch_size=32)
fcst = NeuralForecast(models=[model], freq='ME')
fcst.fit(df=Y_train_df, static_df=AirPassengersStatic, val_size=12)
forecasts = fcst.predict(futr_df=Y_test_df)
# Plot predictions
fig, ax = plt.subplots(1, 1, figsize = (20, 7))
Y_hat_df = forecasts.reset_index(drop=False).drop(columns=['unique_id','ds'])
plot_df = pd.concat([Y_test_df, Y_hat_df], axis=1)
plot_df = pd.concat([Y_train_df, plot_df])
plot_df = plot_df[plot_df.unique_id=='Airline1'].drop('unique_id', axis=1)
plt.plot(plot_df['ds'], plot_df['y'], c='black', label='True')
plt.plot(plot_df['ds'], plot_df['RMoK'], c='blue', label='Forecast')
ax.set_title('AirPassengers Forecast', fontsize=22)
ax.set_ylabel('Monthly Passengers', fontsize=20)
ax.set_xlabel('Year', fontsize=20)
ax.legend(prop={'size': 15})
ax.grid()
```
