Adapting Time Series Foundation Models through Data Mixtures

This project addresses a high-value problem: the 'last mile' performance gap in Time Series Foundation Models (TSFMs). While models like Amazon's Chronos or Google's TimesFM are powerful, they often require domain-specific tuning to reach production-grade accuracy. This repo provides a methodology for using LoRA and specific data mixtures to bridge that gap. However, the defensibility is minimal; it is a research-centric reference implementation with zero stars and only three forks, indicating no current community traction. The primary risk is that frontier labs (OpenAI, Google, Amazon) are already incentivized to release their own 'official' fine-tuning recipes and tools to drive adoption of their base models. Any breakthrough methodology here is likely to be absorbed into the documentation or SDKs of the major TSFM providers within months. For an investor, the value lies in the insight/methodology rather than the code itself, which is trivially reproducible by any ML team familiar with PEFT and time-series data.