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
@article{arxiv.2512.01038,
title = {FMTK: A Modular Toolkit for Composable Time Series Foundation Model Pipelines},
author = {Hetvi Shastri and Pragya Sharma and Walid A. Hanafy and Mani Srivastava and Prashant Shenoy},
journal= {arXiv preprint arXiv:2512.01038},
year = {2025}
}