Reproduce “CometNet: Contextual Motif-guided Long-term Time Series Forecasting” on OpenResearch

About this paper

Weixu Wang, Xiaobo Zhou, Xin Qiao, Lei Wang, Tie Qiu

Tianjin University researchers developed CometNet, a two-module framework that addresses the receptive field bottleneck in long-term time series forecasting by explicitly identifying and leveraging dominant, recurring contextual motifs. The model consistently achieves state-of-the-art performance across eight diverse real-world datasets, demonstrating a particularly strong advantage as forecast horizons extend.

Key insights

• Real-world time series are often governed by "contextual motifs"—recurring, representative long-context patterns that can span thousands of time steps and are crucial for understanding long-term dynamics.
• Explicitly identifying and integrating these global contextual motifs can effectively overcome the receptive field bottleneck, allowing models to leverage long-term dependencies beyond the immediate look-back window.
• A dual instructional signal, comprising both motif routing probabilities and precise temporal positioning within a motif, enables dynamic and contextually-aware guidance for expert networks in a forecasting system.

Summary by alphaXiv.