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.

Links Link para o cabeçalho

Github
PyPi

Installation Link para o cabeçalho

pip install tsknn

Or clone the repository and install locally:

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

Usage example Link para o cabeçalho

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 Link para o cabeçalho

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 Link para o cabeçalho

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 Link para o cabeçalho

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