Time Series Classification And Clustering With Reservoir Computing

Abstract

1. Introduction

This library allows for quick implementation of different architectures for time series data based on Reservoir Computing (RC), the family of approaches popularized in machine learning by Echo State Networks. This library is primarly design to perform classification and clustering of both univariate and multivariate time series. However, it can also be used to perform time series forecasting.

In the following, we present the three main functionalities of this library.

Documentation - Getting Started - Advanced examples

2. Objective

Implement Reservoir Computing models for time series classification, clustering, forecasting, and much more!

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/FilippoMB/Time-series-classification-and-clustering-with-Reservoir-Computing.git
cd Time-series-classification-and-clustering-with-Reservoir-Computing

pip install reservoir-computing

git clone https://github.com/FilippoMB/Time-series-classification-and-clustering-with-Reservoir-Computing.git
cd Time-series-classification-and-clustering-with-Reservoir-Computing
pip install -e .

git clone https://github.com/FilippoMB/Time-series-classification-and-clustering-with-Reservoir-Computing.git
cd Time-series-classification-and-clustering-with-Reservoir-Computing

python examples/classification_example.py

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by FilippoMB and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.