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.
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
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.