pyFAST让时间序列模型轻松处理多源稀疏数据，支持高效实验与扩展。
pyFAST: A Modular PyTorch Framework for Time Series Modeling with Multi-source and Sparse Data
- 数据与模型解耦设计，支持多源、稀疏数据的灵活处理
- 内置稀疏数据对齐融合与专用损失函数，提升建模效率
- 适合做时序分析、医疗/生物信号建模的研究者快速上手
现代时间序列分析需要灵活、高效且可扩展的框架。然而，现有许多Python库在模块化和对不规则、多源或稀疏数据的原生支持方面存在局限。我们提出pyFAST，一个面向研究的PyTorch框架，明确分离数据处理与模型计算，实现更清晰的关注点划分，促进快速实验。其数据引擎针对复杂场景设计，支持多源加载、蛋白质序列处理、高效的序列级与块级填充、动态归一化以及基于掩码的建模（用于插补与预测）。pyFAST整合了受大语言模型启发的无对齐稀疏数据融合架构，提供原生稀疏度度量、专用损失函数和灵活的外生变量融合方式。训练工具包括基于批次的流式聚合评估与设备协同机制，以最大化计算效率。框架内嵌经典与深度学习模型（线性模型、CNN、RNN、Transformer、GNN），采用模块化结构，便于拓展。项目已开源于GitHub，采用MIT许可，为时序研究与应用提供一个紧凑而强大的平台。
原文摘要 · Abstract (English)
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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