pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data

• URL: http://arxiv.org/abs/2508.18891v1
• Date: Tue, 26 Aug 2025 10:05:47 GMT
• Title: pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data
• Authors: Zhijin Wang, Senzhen Wu, Yue Hu, Xiufeng Liu,
• Abstract summary: pyFAST is a research-oriented PyTorch framework for time series analysis.<n>Its data engine is engineered for complex scenarios, supporting multi-source loading, protein sequence handling, efficient sequence- and patch-level padding, dynamic normalization, and mask-based modeling.<n>Released under the MIT license at GitHub, pyFAST provides a compact yet powerful platform for advancing time series research and applications.
• Score: 10.949140998070732
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern time series analysis demands frameworks that are flexible, efficient, and extensible. However, many existing Python libraries exhibit limitations in modularity and in their native support for irregular, multi-source, or sparse data. We introduce pyFAST, a research-oriented PyTorch framework that explicitly decouples data processing from model computation, fostering a cleaner separation of concerns and facilitating rapid experimentation. Its data engine is engineered for complex scenarios, supporting multi-source loading, protein sequence handling, efficient sequence- and patch-level padding, dynamic normalization, and mask-based modeling for both imputation and forecasting. pyFAST integrates LLM-inspired architectures for the alignment-free fusion of sparse data sources and offers native sparse metrics, specialized loss functions, and flexible exogenous data fusion. Training utilities include batch-based streaming aggregation for evaluation and device synergy to maximize computational efficiency. A comprehensive suite of classical and deep learning models (Linears, CNNs, RNNs, Transformers, and GNNs) is provided within a modular architecture that encourages extension. Released under the MIT license at GitHub, pyFAST provides a compact yet powerful platform for advancing time series research and applications.

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