timekan 0.1.2

Python library designed to integrate Kolmogorov Arnold Networks with recurrent mechanisms.

TimeKAN

TimeKAN is a Pytorch implementation of integrating Kolmogorov-Arnold Networks (KAN) for temporal data with recurrent neural network architectures (Currently LSTM and GRU). It is still in an experimental stage, the implementation suffer from exploding gradients and vanishing gradients problems but with careful training it can perform well specifically on non-linear/complex temporal data.

Inspired by:

• Kolmogorov-Arnold Networks
• Chebyshev Polynomial-Based KANs,
• ChebyKAN
• FourierKAN
• TKAN

In tKANLSTM, KAN layers replace the output gate, computing $o_t = \sigma(\text{KAN}(W_x x_t + W_h h_{t-1}))$. In tKANGRU, they form the candidate hidden state, $\tilde{h}t = \tanh(\text{KAN}(W_x x_t + W_h (r_t \odot h{t-1})))$. The layer basis functions can be Fourier series, Chebyshev polynomials, or splines.

Here's how it can perform on Rossler system signal:

The table below compares TimeKAN (using tKANLSTM and spline as basic functions) and a standard bidirectional LSTM on three chaotic datasets available in timekan.utils.datasets. Metrics include Mean Absolute Error (MAE) and training time (seconds) until convergence.

Dataset	Model	MAE	Training Time (s)
Mackey-Glass	LSTM	0.0893	0.3346
	TimeKAN	0.0822	9.8755
Lorenz	LSTM	0.9410	1.1331
	TimeKAN	0.7485	7.9437
Rössler	LSTM	0.3332	1.3951
	TimeKAN	0.2657	12.4172

Installation

Install TimeKAN via pip:

pip install timekan

Alternatively, clone the repository and install locally:

git clone https://github.com/SamerMakni/timekan.git
cd timekan
pip install .

Requirements: Python >= 3.9, PyTorch >= 2.4.0

Usage

Full documentation can be found here.

Here’s a simple example training a TKANLSTM on Mackey-Glass data:

import torch
import torch.nn as nn
from timekan.models.tkan_lstm import tKANLSTM
from timekan.utils.datasets import mackey_glass

class TKANLSTM(nn.Module):
    def __init__(self, input_dim, hidden_dim):
        super().__init__()
        self.tkan = tKANLSTM(
            input_dim=input_dim,
            hidden_dim=hidden_dim,
            return_sequences=False,
            bidirectional=True,
            kan_type='fourier',
            sub_kan_configs={'gridsize': 50, 'addbias': True}
        )
        self.regressor = nn.Linear(hidden_dim * 2, 1)

    def forward(self, x):
        features = self.tkan(x)
        return self.regressor(features).squeeze(-1)

x_train, y_train, x_test, y_test = mackey_glass()

model = TKANLSTM(input_dim=1, hidden_dim=16)
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

for epoch in range(10):
    model.train()
    optimizer.zero_grad()
    outputs = model(x_train)
    loss = criterion(outputs, y_train)
    loss.backward()
    optimizer.step()
    print(f"Epoch {epoch + 1}/10, Training MSE: {loss.item():.4f}")

model.eval()
with torch.no_grad():
    test_outputs = model(x_test)
    test_mse = criterion(test_outputs, y_test).item()
    print(f"Test MSE: {test_mse:.4f}")