This repository implements time series classification and clustering algorithms based on the work of Dr. Eamonn Keogh. It focuses on utilizing a k-Nearest Neighbors (k-NN) algorithm combined with Dynamic Time Warping (DTW) for similarity measurement. The goal is to provide a functional Python implementation for classifying and clustering time series data. A detailed tutorial is also available to guide users through the process.
The project offers a clear and concise implementation of time series classification and clustering, facilitating understanding of the underlying algorithms. Its use of k-NN with DTW is a well-established approach for time series analysis. The included tutorial enhances usability and accelerates learning.
- k-NN Classification: Implements k-NN algorithm for classifying time series based on DTW distance.
- Clustering Algorithms: Provides implementations of clustering algorithms tailored for time series data.
- Python Notebook: Code is organized in Jupyter Notebooks for easy execution and experimentation.
The project is relatively mature, with a core set of algorithms implemented and tested. The last commit occurred in 2021, indicating ongoing maintenance. While the repository isn't actively updated with new features, the existing code is functional and well-documented with a tutorial.
This project benefits data scientists and researchers seeking practical implementations of time series classification and clustering. It addresses the need for readily available tools for analyzing sequential data. The code provides a straightforward approach to classifying and grouping time series based on similarity, offering an alternative to more complex or less accessible methods.