timekan 0.1.0

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

TimeKAN

TimeKAN is a Python library that enhances time series modeling by integrating Kolmogorov-Arnold Networks (KAN) with recurrent neural network architectures like LSTM and GRU. It’s designed to improve prediction accuracy on complex datasets, such as chaotic time series.

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

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

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

class TKANLSTMRegressor(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 = generate_mackey_glass_data()

model = TKANLSTMRegressor(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}")