tsknn 0.1.0

tsknn is a Python implementation of the k-nearest neighbors (KNN) algorithm aimed at time series forecasting

tsknn

TSKNN (Time Series K-Nearest Neighbors) is a Python implementation of the k-nearest neighbors (KNN) algorithm designed specifically for time series forecasting.

It is a pure Python and NumPy reimplementation of the tsfknn package from R—completely rewritten from scratch without relying on C extensions. By leveraging optimized NumPy operations, TSKNN achieves high performance while remaining lightweight and easy to install.

Installation

pip install tsknn

Or clone the repository and install locally:

git clone https://github.com/ricardozago/tsknn.git
cd tsknn
pip install .

Usage example

import numpy as np

from tsknn import tsknn

X = np.random.rand(1000)

model = tsknn(
    cf="mean",  # also median or weighted
    h=24,  # forecast horizon
    transform="multiplicative",  # also additive or None
    msas="recursive",  # also mimo
    lags=[1, 2, 3, 4, 5, 6], # lags to consider as features
    k=[5, 7, 9], # number of neighbors to consider
)
model.fit(X)
tsknn_resp = model.predict()
print(tsknn_resp)

Tests

To run the tests and check coverage:

python -m pytest --cov=tsknn --cov-report term-missing

To run all tests, you’ll need R installed, since the results are compared against the original tsfknn package. You’ll also need the rpy2 library to interface between Python and R.

Contributing

Contributions are welcome! Open issues or pull requests.

1. Fork the project
2. Create your branch (git checkout -b feature/feature-name)
3. Commit your changes (git commit -am 'feat: new feature')
4. Push to the branch (git push origin feature/feature-name)
5. Open a Pull Request

License

This project is licensed under the MIT license. See the LICENSE file for more details.