Sage Former

Abstract

1. Introduction

SageFormer is a novel series-aware graph-enhanced Transformer model designed for long-term forecasting of multivariate time-series (MTS) data. With the proliferation of IoT devices, MTS data has become ubiquitous, necessitating advanced models to forecast future behaviors. SageFormer addresses the challenge of capturing both intra- and inter-series dependencies, enhancing the predictive performance of Transformer-based models.

2. Objective

Code for IoTJ 2024 paper "SageFormer: Series-Aware Framework for Long-Term Multivariate Time-Series Forecasting".

This project demonstrates how Python, Deep 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/zhangzw16/SageFormer.git
cd SageFormer

1. Clone this repository
2. Download datasets from Google Drive or Baidu Drive and place them in the ./dataset folder
3. Create a virtual environment and activate it
4. Install requirements pip install -r requirements.txt
5. Run scripts in the ./scripts folder to train and evaluate the model, for example:
6. Model checkpoints and logs will be saved to outputs folder

sh scripts/long_term_forecast/ECL_script/SageFormer.sh

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 zhangzw16 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.