sigkan 0.1.5

Path Signature-Weighted Kolmogorov-Arnold Networks for Time Series

SigKAN: Path Signature-Weighted Kolmogorov-Arnold Networks for Time Series

This folder includes the original code implemented for the paper of the same name.

The SigKAN is a novel layer that combines the power of path signature and Kolmogorov-Arnold Networks.

The idea behing is to use a learnable path signature that is transformed in weights to the KAN layer.

The Signature is passed through a GRKAN (Gated Residual KAN unit) that is a modified GRN where some Dense layers are replaced by KAN layers.

The signature are computed using iisignature_tensorflow_2 a lightweight wrapper over the iisignature library to create tensorflow 2.x compatible layers for signature function with backward propagation of the gradient.

The code is implemented in tensorflow 2.x and implemnts a custom layer for the SigKAN, that takes an input tensor of shape (baatch_size, time_steps, features) and returns a tensor of shape (batch_size, time_steps, units). The layers can thus be stacked if wanted, however this is not recommended for performances reasons as the iisignature_tensorflow_2 library do not implement GPU acceleration, making made model not XLA compatible.

It is thus important to specify that jit_compile is False in the model.compile() function.

The code is provided as is and is not specially maintained.

Shield:

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.