WaveMoE: A Wavelet-Enhanced Mixture-of-Experts Foundation Model for Time Series Forecasting

WaveMoE addresses a known limitation in current Time Series Foundation Models (TSFMs): the difficulty of modeling both high-frequency localized dynamics and long-term periodicity simultaneously. By using wavelet decomposition as a routing or feature-extraction mechanism within an MoE framework, it allows specialized experts to handle different frequency bands. While technically sound, the project currently has 0 stars and 9 forks (likely internal or immediate academic peers) and is only 5 days old, placing it in the 'research paper' stage. It lacks a structural moat; the core technique—combining wavelets with neural networks—is a well-established concept in signal processing, now applied to the MoE paradigm. It faces extreme competition from frontier labs and major cloud providers who are aggressively releasing TSFMs (e.g., Google's TimesFM, Amazon's Chronos, Salesforce's Moirai). If this wavelet-enhanced approach proves significantly superior, these platforms will likely assimilate the architecture into their next-generation models, leaving this specific repository as a reference rather than a standard. The defensibility is low because there is no proprietary dataset or ecosystem lock-in yet.