arXiv AI August 25, 2026 By ChengAo Shen, Wenchao Yu, Fangyu Wu, Dongjin Song, Hanghang Tong, Dongsheng Luo, Wei Cheng, Haifeng Chen, Jingchao Ni

MetaCaster: Meta-Harness-Optimized Agent for End-to-End Few-Shot Learning of Lightweight Time Series Forecasters

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MetaCaster is a meta-harness-optimized multi-agent framework that enables few-shot learning for lightweight time series forecasters. It uses agentic data generation to automatically train specialized forecasters from only a few examples and textual contexts, positioning agents as intermediary engineers rather than direct forecasters. Experiments on 18 datasets and 23 lightweight forecasters show that MetaCaster achieves data and computational efficiency while maintaining high forecasting quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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