FMTK: A Modular Toolkit for Composable Time Series Foundation Model Pipelines

Overview

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.

Code Map

fmtk/
├── pipeline.py             # Main pipeline implementation
├── metrics.py              # Evaluation metrics
├── utils.py                # Evaluation metrics
├── logger.py               # Memory, Energy logger
├── datasets/
│   └── ecg5000.py          # ECG5000 dataset implementation
│   └── ... 
├── components/
│   ├── backbones/
│   │   └── chronos.py      # Chronos foundation model
│   │   └── ...            
│   ├── encoders/
│   │   └── ...             # Encoders
│   └── decoders/
│       ├── classification/  
│       │   └── ...         # Classification decoders
│       ├── regression/
│       │   └── ...         # Regression decoders
│       └── forecasting/
│           └── ...         # Forecasting decoders

Installation

Clone the repo

cd FMTK
conda create -n fmtk python=3.10
conda activate fmtk
pip install -e .

For working with PPG-BP data related tasks install

pip install pyPPG==1.0.41

Note: There might be a package conflict, but it should still function correctly.

For quick start please check out examples.