Retrieval Augmented Time Series Forecasting

This project is a research-oriented reference implementation of a technique rather than a standalone software product. While the application of RAG to time-series foundation models (TSFMs) is a logical and valuable evolution of the field, it lacks any structural moat. With 0 stars and minimal fork activity (4), the project has failed to build a developer community or ecosystem. The defensibility is extremely low because the core logic—using a vector database to find similar segments and prepending them to a model prompt—is a methodology that can be easily replicated by anyone using existing models like Amazon's Chronos or Google's TimesFM. Frontier labs and hyperscalers who own the underlying foundation models are the most likely to implement this as an optimized inference-time feature. For example, Amazon AWS could natively integrate a 'retrieval' parameter into their Chronos inference API, rendering this standalone implementation obsolete within months. The primary value here is the academic validation of the approach, not the software itself.