用路径签名增强RNN记忆机制，提升时序建模能力
SigGate: Enhancing Recurrent Neural Networks with Signature-Based Gating Mechanisms
- 用可学习的路径签名替代LSTM/GRU的遗忘门和重置门
- 在多个时序任务中优于传统LSTM和GRU模型
- 适合需要精准历史依赖建模的时序分析场景
本文提出一种新方法，通过将路径签名融入循环神经网络（RNN）的门控机制来增强其性能。该方法分别用可学习的路径签名替换Long Short-Term Memory（LSTM）的遗忘门和Gated Recurrent Unit（GRU）的重置门。这些签名能够捕捉完整路径历史的几何特征，为网络内存中的信息流动提供更丰富的上下文。相比仅依赖当前输入与状态的决策方式，该改进使模型能基于完整的过去历史进行记忆控制。实验表明，所提出的Signature-LSTM（SigLSTM）和Signature-GRU（SigGRU）模型在多种序列学习任务中均优于传统对应模型。通过在循环结构中引入路径签名，本方法为时间序列分析与预测应用提供了新的性能提升路径。
原文摘要 · Abstract (English)
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.
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