timesfm-6ae21880·2 events·first seen Aliases: TimesFM
A new arXiv preprint benchmarks six deep learning architectures, two zero-shot foundation models, and statistical baselines on multi-horizon behavioural forecasting from wearable and smartphone data across 800+ participants. Key findings include: no single architecture dominates (PatchTST leads among trained models), TimesFM matches or exceeds trained models zero-shot especially in low-data regimes, and participant-level fine-tuning reduces per-feature RMSE by 16–60%. The study is the first to jointly evaluate modern deep learning, foundation models, and personalisation for this domain.
TimesFM is a pretrained foundation model developed by Google Research specifically for time-series forecasting tasks. The repository has accumulated over 20,000 GitHub stars with 99 new stars today, indicating sustained community interest. It represents Google Research's effort to apply the foundation model paradigm to time-series data rather than language or vision.