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<image alt="logo.png" src="https://raw.githubusercontent.com/GDalforno/KNN-TSPI/main/docs/images/logo.png" width="150rem"/>
K-Nearest Neighbors Time Series Prediction with Invariances (KNN-TSPI) algorithm implementation in python. For details about the model access the [paper](https://www.researchgate.net/publication/300414605_A_Study_of_the_Use_of_Complexity_Measures_in_the_Similarity_Search_Process_Adopted_by_kNN_Algorithm_for_Time_Series_Prediction).
## Installation
### Dependencies
The package depends on the following third party libraries:
- `numpy`
### User Installation
Make sure you have `pip` package manager installed in your environment, then run:
```sh
pip install knn-tspi
```
## Getting Started
Once the package is installed successfully, you can import the `KNeighborsTSPI` class and start forecasting univariate time series in a scikit-learn like manner.
```python
import numpy as np
from knn_tspi import KNeighborsTSPI
data = 0.5 * np.arange(60) + np.random.randn(60)
model = KNeighborsTSPI()
model.fit(data)
y = model.predict(h=5)
```
For more detailed examples on how to use the package access [examples](https://github.com/GDalforno/KNN-TSPI/tree/main/examples).
## Development
### Source Code
Clone the repo with the command:
```
git clone https://github.com/GDalforno/KNN-TSPI.git
```
### Setup
Create a python virtual environment and activate it with the command:
```sh
python3.11 -m venv .venv
source .venv/bin/activate
```
Then, set the enviroment up with the command:
```sh
make setup-dev
```
### Testing
Once the setup is completed, launch the test suite for sanity check with:
```sh
make test-dev
```
### Contributing
You can contributing to the project by opening issues, pull requests and reviewing the code. It will help us a lot if you reference the package on blog posts, articlesm social media, etc.
## License
This project is licensed under the MIT License - see the [License](https://github.com/GDalforno/KNN-TSPI/blob/main/LICENSE.txt) file for details.
## Changelog
See the [changelog](https://github.com/GDalforno/KNN-TSPI/blob/main/CHANGES.txt) for a history of notable changes to knn-tspi.
## Project History
During my research in the field of application of machine learning to forecast time series in 2020, I stumbled with a lack of algorithms and frameworks specialized in this task.
One of my colleagues, [Moisés Rocha](https://github.com/moisesrsantos), send me the paper of a modified KNN for time series prediction along with the experiment code written in MATLAB to help me out with my work. A coupled of days after it, I managed to port the code to both python and R and created this repo to store the resulted files.
Throughtout the years that followed, I have seen a growing interest in this repo, and now, I decided to publish it on pip to make it easier for people to include the model in their time series forecasting toolbox as I did a couple of years ago. As far as I know, there is no other implementation of the KNN-TSPI out there.
I am not planning on creating a CRAN package to distribute the model for the R community anytime soon. With that being said, feel free to implement it yourself if you wish. The core R code can be found [here](https://github.com/GDalforno/KNN-TSPI/tree/main/legacy).
## Communication
- Linkedin: https://www.linkedin.com/in/gabriel-dalforno-7b47ba227/
## References
1. Parmezan, Antonio & Batista, Gustavo. (2015). A Study of the Use of Complexity Measures in the Similarity Search Process Adopted by kNN Algorithm for Time Series Prediction. 45-51. 10.1109/ICMLA.2015.217.
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"description": "[](https://pypistats.org/packages/knn-tspi)\n\n\n[](https://github.com/GDalforno/KNN-TSPI/blob/main/LICENSE.txt)\n\n[](https://pypi.org/project/knn-tspi/)\n[](https://www.python.org/dev/peps/pep-0008/)\n[](https://github.com/GDalforno/KNN-TSPI/actions)\n[](https://codecov.io/gh/GDalforno/KNN-TSPI)\n[](https://github.com/GDalforno/KNN-TSPI/releases)\n\n\n\n\n<image alt=\"logo.png\" src=\"https://raw.githubusercontent.com/GDalforno/KNN-TSPI/main/docs/images/logo.png\" width=\"150rem\"/>\n\nK-Nearest Neighbors Time Series Prediction with Invariances (KNN-TSPI) algorithm implementation in python. For details about the model access the [paper](https://www.researchgate.net/publication/300414605_A_Study_of_the_Use_of_Complexity_Measures_in_the_Similarity_Search_Process_Adopted_by_kNN_Algorithm_for_Time_Series_Prediction).\n\n## Installation\n\n### Dependencies\n\nThe package depends on the following third party libraries:\n\n- `numpy`\n\n### User Installation\n\nMake sure you have `pip` package manager installed in your environment, then run:\n\n```sh\npip install knn-tspi\n```\n\n## Getting Started\n\nOnce the package is installed successfully, you can import the `KNeighborsTSPI` class and start forecasting univariate time series in a scikit-learn like manner.\n\n```python\nimport numpy as np\n\nfrom knn_tspi import KNeighborsTSPI\n\n\ndata = 0.5 * np.arange(60) + np.random.randn(60)\n\nmodel = KNeighborsTSPI()\nmodel.fit(data)\n\ny = model.predict(h=5)\n```\n\nFor more detailed examples on how to use the package access [examples](https://github.com/GDalforno/KNN-TSPI/tree/main/examples).\n\n## Development\n\n### Source Code\n\nClone the repo with the command:\n\n```\ngit clone https://github.com/GDalforno/KNN-TSPI.git\n```\n\n### Setup\n\nCreate a python virtual environment and activate it with the command:\n\n```sh\npython3.11 -m venv .venv\nsource .venv/bin/activate\n```\n\nThen, set the enviroment up with the command:\n\n```sh\nmake setup-dev\n```\n\n### Testing\n\nOnce the setup is completed, launch the test suite for sanity check with:\n\n```sh\nmake test-dev\n```\n\n### Contributing\n\nYou can contributing to the project by opening issues, pull requests and reviewing the code. It will help us a lot if you reference the package on blog posts, articlesm social media, etc.\n\n## License\n\nThis project is licensed under the MIT License - see the [License](https://github.com/GDalforno/KNN-TSPI/blob/main/LICENSE.txt) file for details.\n\n## Changelog\n\nSee the [changelog](https://github.com/GDalforno/KNN-TSPI/blob/main/CHANGES.txt) for a history of notable changes to knn-tspi.\n\n## Project History\n\nDuring my research in the field of application of machine learning to forecast time series in 2020, I stumbled with a lack of algorithms and frameworks specialized in this task.\n\nOne of my colleagues, [Mois\u00e9s Rocha](https://github.com/moisesrsantos), send me the paper of a modified KNN for time series prediction along with the experiment code written in MATLAB to help me out with my work. A coupled of days after it, I managed to port the code to both python and R and created this repo to store the resulted files.\n\nThroughtout the years that followed, I have seen a growing interest in this repo, and now, I decided to publish it on pip to make it easier for people to include the model in their time series forecasting toolbox as I did a couple of years ago. As far as I know, there is no other implementation of the KNN-TSPI out there.\n\nI am not planning on creating a CRAN package to distribute the model for the R community anytime soon. With that being said, feel free to implement it yourself if you wish. The core R code can be found [here](https://github.com/GDalforno/KNN-TSPI/tree/main/legacy).\n\n## Communication\n\n- Linkedin: https://www.linkedin.com/in/gabriel-dalforno-7b47ba227/\n\n## References\n\n1. Parmezan, Antonio & Batista, Gustavo. (2015). A Study of the Use of Complexity Measures in the Similarity Search Process Adopted by kNN Algorithm for Time Series Prediction. 45-51. 10.1109/ICMLA.2015.217.\n",
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