PatchTST
patchtst-4bcdab17·3 events·first seen 28d agoAliases: PatchTST
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Patch Time Series Transformer in Hugging Face
Hugging Face has integrated PatchTST, a patch-based Transformer architecture for time series forecasting, into its ecosystem. PatchTST applies the patching concept from vision transformers to time series data, dividing sequences into subseries-level patches as input tokens. The blog post covers usage, fine-tuning, and zero-shot transfer capabilities of the model within the Hugging Face Transformers library.
Benchmark of deep learning architectures for multi-horizon behavioural forecasting in mobile health
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.
PatchTSMixer in HuggingFace
Hugging Face introduces PatchTSMixer, a lightweight MLP-Mixer-based model for multivariate time-series forecasting, now available in the Transformers library. The model is designed for efficient patch-based mixing of temporal and channel information. This integration expands Hugging Face's time-series modeling capabilities alongside the previously added PatchTST model.