NEWS · MODELS · #296
Google Research presents TimesFM, a 200M-parameter decoder-only foundation model for time-series forecasting
Google Research researchers Rajat Sen and Yichen Zhou introduce TimesFM, a decoder-only transformer foundation model pre-trained on a corpus of about 100 billion real-world time-points. At 200 million parameters, TimesFM treats contiguous time-point patches as tokens and achieves strong zero-shot forecasting on diverse unseen datasets—approaching state-of-the-art supervised methods—with plans to make the model available via Google Cloud Vertex AI later this year.
KEY POINTS
- Google Research researchers Rajat Sen and Yichen Zhou introduce TimesFM, a decoder-only transformer foundation model pre-trained on a corpus of about 100 billion real-world time-points.
- At 200 million parameters, TimesFM treats contiguous time-point patches as tokens and achieves strong zero-shot forecasting on diverse unseen datasets—approaching state-of-the-art supervised methods—with plans to make the model available via Google Cloud Vertex AI later this year.
- A pretrained time-series foundation model that delivers competitive zero-shot forecasts could reduce the need for dataset-specific retraining and speed deployment of forecasting in domains like retail and finance.
WHY IT MATTERS
A pretrained time-series foundation model that delivers competitive zero-shot forecasts could reduce the need for dataset-specific retraining and speed deployment of forecasting in domains like retail and finance.