Tslumen

Abstract

1. Introduction

series data with rich, pre-canned artifacts, packed with charts and statistical information. The primary goal of tslumen is to expedite and bring consistency to how time series EDA is performed, allowing you to uncover the fundamental aspects in seconds rather than hours or days.

Refer to the Quick Start page of the documentation for a brief tour of the package.

Complete example notebooks can be found on the User Guide section of the documentation.

2. Objective

A library for Time Series EDA (exploratory data analysis)

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

4. Technology Stack

• Platform agnostic, integrates nicely with your datascience workspace
• Built on open source technology and research
• Highly customizable and extensible
• Data (profiling results) completely detached from the visuals
• Can be executed from the command line
• Efficient execution using parallel processing
• Includes a great number of statistical information, including descriptive statistics statistical tests like KPSS or ADF, correlation, tsfeatures, etc.
• Various plots specifically tailored to time series analysis

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/hsbc/tslumen.git
cd tslumen

pip install -U tslumen

# cd into tslumen after cloning the repo
make install

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 hsbc and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.