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https://github.com/Zeying-Gong/PatchMixer
About Code release for "PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting"
https://github.com/Zeying-Gong/PatchMixer
Last synced: 3 months ago
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About Code release for "PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting"
- Host: GitHub
- URL: https://github.com/Zeying-Gong/PatchMixer
- Owner: Zeying-Gong
- License: mit
- Created: 2023-10-08T02:02:00.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2024-06-27T00:08:49.000Z (6 months ago)
- Last Synced: 2024-06-27T15:27:20.553Z (6 months ago)
- Language: Python
- Size: 2.14 MB
- Stars: 151
- Watchers: 10
- Forks: 14
- Open Issues: 4
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- StarryDivineSky - Zeying-Gong/PatchMixer - Mixing 架构。PatchMixer 主要由两个卷积层和两个预测头组成。它的显着特点是“补丁混合”设计，这意味着模型最初将输入时间序列分割成更小的时间补丁，然后集成来自这些补丁内部和之间的信息。从定量上看，与最先进的 Transformer （PatchTST） 相比，PatchMixer 在 MSE 上总体上相对减少了 3.9%，在 MAE 上相对减少了 3.0%。当根据性能最佳的基于 MLP 的模型 （DLinear） 进行评估时，我们的模型显示 MSE 上的 11.6% 和 MAE 上的 9.4% 总体下降。此外，与基于CNN的最佳模型（TimesNet）的可实现结果相比，我们证明了MSE上21.2%和MAE上12.5%的总体相对减少显着。 (时间序列 / 网络服务_其他)
README
# PatchMixer
## Introduction
This is the official implementation of PatchMixer: [PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting](https://arxiv.org/abs/2310.00655).
## Model Overview
PatchMixer is primarily composed of two convolutional layers and two forecasting heads. Its distinguishing feature is the “patch-mixing” design, which means the model initially segments the input time series into smaller temporal patches and subsequently integrates information from both within and between these patches.
## Getting Started
1. Install requirements.
```
pip install -r requirements.txt
```
2. Download data. You can download all the datasets from [Autoformer](https://drive.google.com/drive/folders/1ZOYpTUa82_jCcxIdTmyr0LXQfvaM9vIy). Create a seperate folder ```./dataset``` and put all the csv files in the directory.
3. Training. All the scripts are in the directory ```./scripts/PatchMixer```. For example, if you want to get the multivariate forecasting results for weather dataset, just run the following command, and you can open ```./result.txt``` to see the results once the training is done, log file is in ```./logs/LongForecasting/*.log```:
```
sh ./scripts/PatchMixer/weather.sh
```
You can also add ```--use_multi_gpu``` for multi-gpu training. The hyperparameters can be adjusted as your needs (e.g. different patch length, different sequence lengths and prediction lengths.). We also provide codes for the baseline models.
## Results
### 🏆 Achieve state-of-the-art in Long-Term Time series Forecasting
Quantitatively, PatchMixer demonstrates an overall relative reduction of $\mathbf{3.9\%}$ on MSE and $\mathbf{3.0\%}$ on MAE in comparison to the state-of-the-art Transformer (PatchTST). When evaluated against the best-performing MLP-based model (DLinear), our model showcases an overall decline of $\mathbf{11.6\%}$ on MSE and $\mathbf{9.4\%}$ on MAE. Moreover, in comparison to the achievable outcomes with the best CNN-based model (TimesNet), we demonstrate a remarkable overall relative reduction of $\mathbf{21.2\%}$ on MSE and $\mathbf{12.5\%}$ on MAE.
### 🌟 Training and Inference Efficiency
Our results highlight two key improvements. First, PatchMixer achieves a 3x faster inference and 2x faster training speed compared to PatchTST. Second, PatchTST's performance is highly sensitive to the length of the look-back window, particularly when it reaches or exceeds 1440. In contrast, PatchMixer exhibits fewer fluctuations in both inference and training times with increasing historic length, contributing to higher accuracy and computational efficiency.
### 🌟 Efficiency on Long Look-back Windows
In principle, the large receptive field is beneficial for improving performance, while the receptive field of the look-back window in time series analysis is also important. Generally speaking, a powerful LTSF model with a strong temporal relation extraction capability should be able to achieve better results with longer input historical sequences. Recent baselines such as PatchTST, DLinear, and our PatchMixer consistently reduce the MSE scores as the receptive field increases, which confirms our model's capability to learn from the longer look-back window.
## Acknowledgement
We appreciate the following GitHub repo very much for the valuable code base and datasets:
https://github.com/yuqinie98/PatchTST
https://github.com/wanghq21/MICN
https://github.com/thuml/TimesNet
https://github.com/cure-lab/LTSF-Linear
https://github.com/zhouhaoyi/Informer2020
https://github.com/thuml/Autoformer
https://github.com/MAZiqing/FEDformer
https://github.com/ts-kim/RevIN
## Contact
If you have any questions or concerns, please submit an issue on GitHub. For matters that require more direct communication, you can also email us at [[email protected]](mailto:[email protected]). However, we kindly encourage the use of issues for better transparency and tracking.
## Citation
If you find this repository useful in your research, please consider citing our paper:
```
@inproceedings{Gong2023PatchMixerAP,
title={PatchMixer: A Patch-Mixing Architecture for Long-Term Time Series Forecasting},
author={Zeying Gong and Yujin Tang and Junwei Liang},
year={2023},
url={https://api.semanticscholar.org/CorpusID:263334059}
}
```