Many Minds, One Goal: Time Series Forecasting via Sub-task Specialization and Inter-agent Cooperation

Yang Wang (Concordia University) · Binwu Wang (University of Science and Technology of China) · Kuo Yang (University of Science and Technology of China) · Zhengyang Zhou (University of Science and Technology of China) · Qihe Huang (University of Science and Technology China) · Yangze Li (University of Science and Technology of China)

adaptable forecastscommunication topologycooperative reasoningforecasting resolutionsgeneralizationheterogeneous signal perceptionlightweight voting aggregatormodel architecturemulti-agent forecasting systemmulti-scale forecastingrobust forecastsstructural variationssub-taskstemporal diversitytime series forecasting

Time series forecasting is a critical and complex task, characterized by diverse temporal patterns, varying statistical properties, and different prediction horizons across datasets and domains. Conventional approaches typically rely on a single, unified model architecture to handle all forecasting scenarios. However, such monolithic models struggle to generalize across dynamically evolving time series with shifting patterns. In reality, different types of time series may require distinct modeling strategies. Some benefit from homogeneous multi-scale forecasting awareness, while others rely on more complex and heterogeneous signal perception. Relying on a single model to capture all temporal diversity and structural variations leads to limited performance and poor interpretability. To address this challenge, we propose a Multi-Agent Forecasting System (MAFS) that abandons the one-size-fits-all paradigm. MAFS decomposes the forecasting task into multiple sub-tasks, each handled by a dedicated agent trained on specific temporal perspectives (e.g., different forecasting resolutions or signal characteristics). Furthermore, to achieve holistic forecasting, agents share and refine information through different communication topology, enabling cooperative reasoning across different temporal views. A lightweight voting aggregator then integrates their outputs into consistent final predictions. Extensive experiments across 11 benchmarks demonstrate that MAFS significantly outperforms traditional single-model approaches, yielding more robust and adaptable forecasts.