FMTK让时间序列大模型组装像搭积木一样简单，7行代码搞定复杂任务
FMTK: A Modular Toolkit for Composable Time Series Foundation Model Pipelines
- 用标准化组件抽象实现时间序列大模型模块化搭建
- 仅需平均7行代码即可完成完整训练管道构建
- 适合需要快速实验和复现的时序建模研究者
基础模型（FMs）因其能在极少或无需微调的情况下适应新任务，为机器学习应用开辟了新途径。时间序列基础模型（TSFMs）——在时间序列数据上训练的基础模型——在分类、回归和缺失值填补任务中表现出色。近期的流水线通过结合TSFMs与特定任务的编码器、解码器和适配器来提升性能；然而，这类流水线的构建通常依赖于特定模型的定制实现，阻碍了模块化和可复现性。我们提出FMTK，一个开源、轻量且可扩展的工具包，通过标准化的主干模型和组件抽象，实现时间序列基础模型流水线的构建与微调。FMTK支持跨模型和任务的灵活组合，在保证正确性和性能的同时，平均仅需七行代码即可完成配置。
原文摘要 · Abstract (English)
Foundation models (FMs) have opened new avenues for machine learning applications due to their ability to adapt to new and unseen tasks with minimal or no further training. Time-series foundation models (TSFMs) -- FMs trained on time-series data -- have shown strong performance on classification, regression, and imputation tasks. Recent pipelines combine TSFMs with task-specific encoders, decoders, and adapters to improve performance; however, assembling such pipelines typically requires ad hoc, model-specific implementations that hinder modularity and reproducibility. We introduce FMTK, an open-source, lightweight and extensible toolkit for constructing and fine-tuning TSFM pipelines via standardized backbone and component abstractions. FMTK enables flexible composition across models and tasks, achieving correctness and performance with an average of seven lines of code. https://github.com/umassos/FMTK
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