TimesFM-3 forecasts complex, related data series in a single pass, beating older models on major tests.
Google Research introduced TimesFM-3, a 330 million parameter model built for multivariate time series forecasting, handling multiple related data streams in one forward pass. It tops benchmarks like GIFT-Eval and the TIME leaderboard.
Unlike earlier versions, it can factor in both past data, like foot traffic history, and future info, like upcoming holidays. Weights are open on Hugging Face for research, though commercial use needs a license.
Key Capabilities:
- Multivariate Forecasting: Predicts several related data series together in a single pass, not one at a time.
- Context Aware: Incorporates future known events like holidays alongside historical data for sharper predictions.
- Open for Research: Weights are freely available on Hugging Face, with a commercial license required for production.