FMTK: A Modular Toolkit for Composable Time Series Foundation Model Pipelines
This toolkit addresses the need for standardized and reproducible pipelines in time-series machine learning, though it is incremental as it builds on existing foundation model concepts.
The paper tackles the problem of ad hoc, model-specific implementations hindering modularity and reproducibility in time-series foundation model pipelines by introducing FMTK, an open-source toolkit that enables flexible composition across models and tasks with an average of seven lines of code.
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