Chaotic time series prediction based on multi-scale attention in a multi-agent environment

Hua Miao, Wei Zhu, Yuanhong Dan and Nanxiang Yu

Chaos, Solitons & Fractals, 2024, vol. 183, issue C

Abstract: A new problem at the intersection of multi-agent systems, chaotic time series prediction, and flow map learning is formulated in this paper. The problem involves agents collaborating to track moving targets in chaotic dynamic systems by communicating. Inspired by the multi-scale hierarchical time-stepper (HiTS), a novel Distributed Prediction Network based on Multi-scale Attention (DPNMA) is proposed to fuse predictions from agents at different scales through an enhanced self-attention mechanism. The experimental evaluation demonstrates that DPNMA effectively mitigates cumulative errors and enhances the accuracy and robustness of the predictions, which has important implications for the scenarios where the agents have heterogeneous and constrained capabilities.

Keywords: Chaos systems; Multi-step prediction; Flow maps; Self attention; Multi-Agent Systems (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:183:y:2024:i:c:s0960077924004272

DOI: 10.1016/j.chaos.2024.114875

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