Abstract
Time Mixer is an open-source Data Science project. [ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting". TimeMixer, as a fully MLP-based architecture, taking full advantage of disentangled multiscale time series, is proposed to achieve consistent SOTA performances in both long and short-term forecasting tasks with favorable run-time efficiency. It is built using Python, Deep Learning, Machine Learning. The complete source code is publicly available on GitHub under the Apache License 2.0, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
TimeMixer, as a fully MLP-based architecture, taking full advantage of disentangled multiscale time series, is proposed to achieve consistent SOTA performances in both long and short-term forecasting tasks with favorable run-time efficiency.
(ICLR'24) TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
News (2024.10): TimeMixer series has been included in PyPOTS. Many thanks to the PyPOTS team.
2. Objective
[ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting"
This project demonstrates how Python, Deep Learning, Machine Learning 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/kwuking/TimeMixer.git
cd TimeMixerFull 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.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by kwuking 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.
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