Time Series Works Conferences

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)

Data ScienceMulti-languageMIT

Abstract

Time Series Works Conferences is an open-source Data Science project. Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.). My research is driven by a strong passion for Time-Series Research and Deep Learning. Currently, my work focuses on developing next-generation Time-Series Foundation Models (TSFMs), generative modeling, and scalable frameworks for complex temporal and spatio-temporal data mining. It is built using Deep Learning. The complete source code is publicly available on GitHub under the MIT License, making it a useful reference for students building a Data Science mini project or final-year project.

1. Introduction

My research is driven by a strong passion for Time-Series Research and Deep Learning. Currently, my work focuses on developing next-generation Time-Series Foundation Models (TSFMs), generative modeling, and scalable frameworks for complex temporal and spatio-temporal data mining.

I am a Ph.D. student in the School of Computer Science and Engineering (CSE) at UNSW Sydney, Australia, where I am fortunate to be co-supervised by Prof. Flora Salim and Dr. Hao Xue.

Prior to joining UNSW, I received my Master's degree, during which I had the privilege of being advised by Prof. Xuan Song, Dr. Quanjun Chen, and Prof. Renhe Jiang.

2. Objective

Time-Series Work Summary in CS Top Conferences (NIPS, ICML, ICLR, KDD, AAAI, WWW, IJCAI, CIKM, ICDM, ICDE, etc.)

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

4. Technology Stack

Deep Learning

5. System Requirements

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

  • See the project README for exact requirements
  • Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/lixus7/Time-Series-Works-Conferences.git
cd Time-Series-Works-Conferences

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 lixus7 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.

View Source on GitHubDownload ZIP

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