AI Briefingtimesfm: Ranked #1 on fev-bench, supports multivariate time series forecasting
google-research/timesfm
About the project
This is a time series foundation model released by Google Research. It uses pre-trained weights to forecast time series data across various domains without requiring separate tuning.
Starting with version 3.0, TimesFM natively supports multivariate forecasting and covariate handling. It accepts both past-only covariates and future-known covariates, handling both univariate and multivariate scenarios.
It has achieved the top rank on major benchmarks including fev-bench, TIME Benchmark, and GIFT-Eval. It provides zero-shot generalization performance validated on over 100 real-world tasks and 50 domain datasets.
It is provided as a PyTorch-based library, with weights available for download on Hugging Face Hub. However, the weights for version 3.0 are subject to a license permitting use for non-profit and non-commercial purposes only.
google-research/timesfm
TimesFM (Time Series Foundation Model) is a pretrained time-series foundation model developed by Google Research for time-series forecasting.
Python
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