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

TL;DR

pyFAST is a flexible, modular PyTorch framework designed for complex time series analysis, supporting multi-source, sparse, and irregular data with advanced modeling and training utilities.

Contribution

It introduces a decoupled architecture that enhances flexibility and extensibility for time series modeling with multi-source and sparse data.

Findings

Supports multi-source and sparse data processing

Includes a comprehensive suite of classical and deep learning models

Facilitates rapid experimentation and extension

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.