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

arXiv: https://arxiv.org/abs/2406.17890 · HTML: https://mlsanity.com/paper/2406.17890/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 25 Jun 2024.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)](https://mlsanity.com/paper/1511.07289/) (2015)
- [Sigmoid-Weighted Linear Units for Neural Network Function Approximation in Reinforcement Learning](https://mlsanity.com/paper/1702.03118/) (2017)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Language Modeling with Gated Convolutional Networks](https://mlsanity.com/paper/1612.08083/) (2016)
- [KAN: Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2404.19756/) (2024)
- [Wav-KAN: Wavelet Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2405.12832/) (2024)
- [A Primer on the Signature Method in Machine Learning](https://mlsanity.com/paper/1603.03788/) (2016)
- [Kolmogorov-Arnold Networks (KANs) for Time Series Analysis](https://mlsanity.com/paper/2405.08790/) (2024)
- [TKAN: Temporal Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2405.07344/) (2024)
- [A Temporal Kolmogorov-Arnold Transformer for Time Series Forecasting](https://mlsanity.com/paper/2406.02486/) (2024)
- [Autoregressive Convolutional Neural Networks for Asynchronous Time Series](https://mlsanity.com/paper/1703.04122/) (2017)
- [Embedding and learning with signatures](https://mlsanity.com/paper/1911.13211/) (2019)
- [Developing the Path Signature Methodology and its Application to Landmark-based Human Action Recognition](https://mlsanity.com/paper/1707.03993/) (2017)
- [A signature-based machine learning model for bipolar disorder and borderline personality disorder](https://mlsanity.com/paper/1707.07124/) (2017)

## Built on by (most cited first, 13 of 13)

- [A Survey on Kolmogorov-Arnold Network](https://mlsanity.com/paper/2411.06078/) (2024)
- [PowerMLP: An Efficient Version of KAN](https://mlsanity.com/paper/2412.13571/) (2024)
- [Exploring Kolmogorov-Arnold Networks for Interpretable Time Series Classification](https://mlsanity.com/paper/2411.14904/) (2024)
- [A Gated Residual Kolmogorov-Arnold Networks for Mixtures of Experts](https://mlsanity.com/paper/2409.15161/) (2024)
- [A Temporal Linear Network for Time Series Forecasting](https://mlsanity.com/paper/2410.21448/) (2024)
- [Deep Learning for VWAP Execution in Crypto Markets: Beyond the Volume Curve](https://mlsanity.com/paper/2502.13722/) (2025)
- [SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms](https://mlsanity.com/paper/2502.09318/) (2025)
- [Kolmogorov Arnold Networks in Fraud Detection: Bridging the Gap Between Theory and Practice](https://mlsanity.com/paper/2408.10263/) (2024)
- [Recurrent Neural Networks for Dynamic VWAP Execution: Adaptive Trading Strategies with Temporal Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2502.18177/) (2025)
- [VWAP Execution with Signature-Enhanced Transformers: A Multi-Asset Learning Approach](https://mlsanity.com/paper/2503.02680/) (2025)
- [Hardware-Oriented Inference Complexity of Kolmogorov-Arnold Networks](https://mlsanity.com/paper/2604.03345/) (2026)
- [HKAN: Hierarchical Kolmogorov-Arnold Network without Backpropagation](https://mlsanity.com/paper/2501.18199/) (2025)
- [Fully Kolmogorov-Arnold Deep Model in Medical Image Segmentation](https://mlsanity.com/paper/2602.03156/) (2026)

## Abstract (arXiv)

We propose a novel approach that enhances multivariate function approximation using learnable path signatures and Kolmogorov-Arnold networks (KANs). We enhance the learning capabilities of these networks by weighting the values obtained by KANs using learnable path signatures, which capture important geometric features of paths. This combination allows for a more comprehensive and flexible representation of sequential and temporal data. We demonstrate through studies that our SigKANs with learnable path signatures perform better than conventional methods across a range of function approximation challenges. By leveraging path signatures in neural networks, this method offers intriguing opportunities to enhance performance in time series analysis and time series forecasting, among other fields.