Retrieval-Augmented Forecasting of Time-series

Last Updated on March 4, 2026 by Editorial Team Author(s): DrSwarnenduAI Originally published on Towards AI. RAFT proves that time series forecasting doesn’t need bigger weights — it needs a better library card Here’s the thing about The Cheesecake Factory menu: it’s 21 pages long. New Frontier in Time seriesThe article discusses a novel approach to time series forecasting called RAFT (Retrieval-Augmented Forecasting of Time-series), which posits that instead of relying on models with larger parameter counts to memorize patterns, it’s more effective to implement a retrieval system. This method allows the model to access relevant historical data rather than overfitting and forgetting critical rare events, significantly enhancing its performance while maintaining a lightweight architecture compared to traditional models like Transformers. Read the full blog for free on Medium. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI