Google's new AI model predicts the future from sales data, weather, and discount schedules
Google Research released TimesFM-3, a 330M-parameter multivariate time series forecasting model that tops Gift-Eval, FEV-Bench, and Time benchmarks and is on Hugging Face.
Google Research released TimesFM-3, a 330-million-parameter Transformer-based time series forecasting model trained on more than one trillion real and synthetic data points. It works zero-shot and adds multivariate support, ingesting related series, historical-only covariates, and known future events such as discount schedules and weather forecasts, while filling all future time steps in a single one-shot pass. Google reports first place among pretrained forecasting models on Gift-Eval, FEV-Bench, and Time, ahead of Amazon's Chronos-2, the Toto-2.0 family, and its own TimesFM-2.5. Weights are available on GitHub and Hugging Face, with BigQuery integration planned in the coming weeks.
- TimesFM-3 has 330 million parameters and was trained on over one trillion real and synthetic time series data points.
- Multivariate architecture combines related series, historical-only covariates, and known future events like discounts and weather forecasts.
- One-shot decoding fills all future time steps in a single pass, replacing error-prone step-by-step forecasting.
- Ranks first on Gift-Eval, FEV-Bench, and Time benchmarks against Amazon Chronos-2 and the Toto-2.0 family.
- Weights are on GitHub and Hugging Face; BigQuery integration is planned within weeks.
- Organizations
- Google ResearchAmazon
- AI models
- TimesFM-3
From the original article
3 min read at the-decoder.com
Google Research has released TimesFM-3, a forecasting model that analyzes time series alongside related data and known future events like sales promotions or weather forecasts. Instead of predicting the future step by step, the 330-million-parameter model fills in all future time points in a single pass, which cuts compute time and reduces compounding errors.
By Jonathan Kemper · the-decoder.com
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