# SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms

arXiv: https://arxiv.org/abs/2502.09318 · HTML: https://mlsanity.com/paper/2502.09318/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 13 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [A Primer on the Signature Method in Machine Learning](https://mlsanity.com/paper/1603.03788/) (2016)
- [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)
- [Embedding and learning with signatures](https://mlsanity.com/paper/1911.13211/) (2019)
- [SigKAN: Signature-Weighted Kolmogorov-Arnold Networks for Time Series](https://mlsanity.com/paper/2406.17890/) (2024)
- [Rough Transformers for Continuous and Efficient Time-Series Modelling](https://mlsanity.com/paper/2403.10288/) (2024)
- [Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units](https://mlsanity.com/paper/2002.11338/) (2020)
- [Keras Sig: Efficient Path Signature Computation on GPU in Keras 3](https://mlsanity.com/paper/2501.08455/) (2025)
- [Clustering Digital Assets Using Path Signatures: Application to Portfolio Construction](https://mlsanity.com/paper/2410.23297/) (2024)

## Built on by (most cited first, 3 of 3)

- [Deep Learning for VWAP Execution in Crypto Markets: Beyond the Volume Curve](https://mlsanity.com/paper/2502.13722/) (2025)
- [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)

## Abstract (arXiv)

In this paper, we propose a novel approach that enhances recurrent neural networks (RNNs) by incorporating path signatures into their gating mechanisms. Our method modifies both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures by replacing their forget and reset gates, respectively, with learnable path signatures. These signatures, which capture the geometric features of the entire path history, provide a richer context for controlling information flow through the network's memory. This modification allows the networks to make memory decisions based on the full historical context rather than just the current input and state. Through experimental studies, we demonstrate that our Signature-LSTM (SigLSTM) and Signature-GRU (SigGRU) models outperform their traditional counterparts across various sequential learning tasks. By leveraging path signatures in recurrent architectures, this method offers new opportunities to enhance performance in time series analysis and forecasting applications.