DevOps / SRE / Platform · 31.08.2026, 19:48 UTC
Google’s new forecasting model beats everyone. You can’t use it at work (yet).
| Schweregrad | info |
|---|---|
| Kategorie | DevOps / SRE / Platform |
| Quelle | The New Stack ↗ |
| Veröffentlicht | 31.08.2026 UTC |
Sicherheitsmeldung mit Schweregrad noch nicht bewertet. Technische Details im Tab „Originaltext“; empfohlene Schritte in der Checkliste.
On Monday, Google launched TimesFM-3, a 330-million-parameter time-series forecasting model trained on over a trillion real-world and synthetic data time points.
The new model is now available on Hugging Face under a non-commercial license.
Large language models are great at predicting the next word. For businesses, time-series forecasting models essentially try to do the same thing, but for data. Over the last few years, there’s been a lot of work in building better forecasting models. Last year saw the launch of models like Chronos-2 from Amazon and Moirai 2.0 from Salesforce, while, more recently, Datadog launched its Toto 2.0 model.
These models represent a relatively new breed of forecasting models in that they can ingest multiple time series. As Google research scientists Ayush Jain and Rajat Sen explain in the announcement, “most real-world forecasting problems are inherently multivariate: where multiple time series and auxiliary external features jointly impact the future forecast of a time series.”
Past sales, they explain, only tell part of the story. “A good forecast should also draw on sales of related products (e.g., ice cream cones, syrups), historical foot traffic, and known future events like weather forecasts, promotions, and holidays.”
TimesFM-3 is Google’s first model that was natively pre-trained to handle these multiple time series and do so with zero-shot generalizations. This also allows it to forecast multiple related time series in parallel and include historical data like past foot traffic.
In the benchmarks Google shared, TimesFM-3 …