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.

翻译：时间序列分析支撑着众多现实世界应用，然而现有的时序专用方法以及基于预训练大模型的方案在整合直观视觉推理、通过自适应工具使用实现跨任务泛化方面仍存在局限。为应对这些不足，我们提出MAS4TS——一个面向通用时间序列任务的工具驱动型多智能体系统，其构建于分析器-推理器-执行器范式之上，将智能体通信、视觉推理与潜在空间重构整合在统一框架内。MAS4TS首先利用视觉语言模型对带有结构化先验的时间序列图表进行视觉推理以提取时序结构，随后在潜在空间中重构预测轨迹。三个专用智能体通过共享内存与门控通信进行协同，同时路由器为执行过程选择任务特定的工具链。在多个基准测试上的大量实验表明，MAS4TS在广泛的时间序列任务中取得了最先进的性能，同时展现出强大的泛化能力与高效的推理效率。