Visual Reasoning over Time Series via Multi-Agent System

2026/02/03 by Weilin Ruan, Yuxuan Liang · 1 voice
Computer Science · #Data Visualization and Analytics #Multimodal Machine Learning Applications #Time Series Analysis and Forecasting #cs.AI #cs.MA

paper · pdf · doi:10.48550/arxiv.2602.03026

openalex publication_date 2026/02/03 · arxiv published 2026/02/03 · arxiv updated 2026/02/03 · openalex created_date 2026/02/06 · openalex updated_date 2026/07/28

Abstract

Time series analysis underpins many real-world applications, yet existing time-series-specific methods and pretrained large-model-based approaches remain limited in integrating intuitive visual reasoning and generalizing across tasks with adaptive tool usage. To address these limitations, we propose MAS4TS, a tool-driven multi-agent system for general time series tasks, built upon an Analyzer-Reasoner-Executor paradigm that integrates agent communication, visual reasoning, and latent reconstruction within a unified framework. MAS4TS first performs visual reasoning over time series plots with structured priors using a Vision-Language Model to extract temporal structures, and subsequently reconstructs predictive trajectories in latent space. Three specialized agents coordinate via shared memory and gated communication, while a router selects task-specific tool chains for execution. Extensive experiments on multiple benchmarks demonstrate that MAS4TS achieves state-of-the-art performance across a wide range of time series tasks, while exhibiting strong generalization and efficient inference.

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Discussions

• Visual Reasoning over Time Series via Multi-Agent System - By arXiv https://arxiv.org/abs/2602.03026 time series analysis, multi-agent systems, visual reasoning, machine learning, data science [bsky, 2 points, 0 comments]

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