https://github.com/zhangzw16/sageformer
Code for IoTJ 2024 paper "SageFormer: Series-Aware Framework for Long-Term Multivariate Time-Series Forecasting".
https://github.com/zhangzw16/sageformer
deep-learning time-series-forecasting
Last synced: 7 months ago
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Code for IoTJ 2024 paper "SageFormer: Series-Aware Framework for Long-Term Multivariate Time-Series Forecasting".
- Host: GitHub
- URL: https://github.com/zhangzw16/sageformer
- Owner: zhangzw16
- License: mit
- Created: 2024-02-20T06:34:02.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-29T10:18:51.000Z (over 1 year ago)
- Last Synced: 2025-03-24T02:06:42.865Z (8 months ago)
- Topics: deep-learning, time-series-forecasting
- Language: Python
- Homepage: https://ieeexplore.ieee.org/abstract/document/10423755
- Size: 60.5 KB
- Stars: 58
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SageFormer: Series-Aware Framework for Long-Term Multivariate Time-Series Forecasting
This repository contains the code for the paper "[SageFormer: Series-Aware Framework for Long-Term Multivariate Time-Series Forecasting](https://ieeexplore.ieee.org/abstract/document/10423755)" by Zhenwei Zhang, Linghang Meng, and Yuantao Gu, published in the IEEE Internet of Things Journal.
## 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.
## Usage
To train and evaluate the SageFormer model:
- Clone this repository
- Download datasets from [Google Drive](https://drive.google.com/drive/folders/13Cg1KYOlzM5C7K8gK8NfC-F3EYxkM3D2) or [Baidu Drive](https://pan.baidu.com/share/init?surl=r3KhGd0Q9PJIUZdfEYoymg&pwd=i9iy) and place them in the `./dataset` folder
- Create a virtual environment and activate it
- Install requirements `pip install -r requirements.txt`
- Run scripts in the `./scripts` folder to train and evaluate the model, for example:
```bash
sh scripts/long_term_forecast/ECL_script/SageFormer.sh
```
- Model checkpoints and logs will be saved to outputs folder
## Contacts
For any questions, please contact the authors at `zzw20 [at] mails.tsinghua.edu.cn` or write a [discussion on github](https://github.com/zhangzw16/SageFormer/discussions).
## Citation
If you find this code or paper useful for your research, please cite:
```bibtex
@ARTICLE{zhang2024sageformer,
author={Zhang, Zhenwei and Meng, Linghang and Gu, Yuantao},
journal={IEEE Internet of Things Journal},
title={SageFormer: Series-Aware Framework for Long-Term Multivariate Time Series Forecasting},
year={2024},
doi={10.1109/JIOT.2024.3363451}}
```
# Acknowledgement
This library is constructed based on the following repos:
- https://github.com/thuml/Time-Series-Library
- https://github.com/PatchTST/PatchTST