https://github.com/wanghq21/MICN

Code release of paper "MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting" (ICLR 2023)
https://github.com/wanghq21/MICN

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Code release of paper "MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting" (ICLR 2023)

• Host: GitHub
• URL: https://github.com/wanghq21/MICN
• Owner: wanghq21
• Created: 2023-02-09T01:50:17.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2023-02-11T12:31:24.000Z (over 1 year ago)
• Last Synced: 2024-08-02T06:19:35.650Z (3 months ago)
• Language: Python
• Size: 1.06 MB
• Stars: 94
• Watchers: 3
• Forks: 11
• Open Issues: 12
• Metadata Files:
  • Readme: README.md

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# MICN

Code release of paper ["MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting"](https://openreview.net/pdf?id=zt53IDUR1U) (ICLR 2023 oral)

MICN achieve SOTA on six benchmarks.

##  Overall Architecture

As shown in Figure 1, we decompose the time series into seasonal part and trend part by Multi-scale Hybrid Decomposition. For seasonal part, we use Seasonal Prediction block to predict. For trend part, we use simple regression to predict. 











### Seasonal Prediction block

The seasonal part contains several different patterns after Multi-scale Hybrid Decomposition. For each pattern, we use local-global module to extract local information and global correlations. 











#### Local-Global module

We use downsampling convolution to extract local features and isometric convolution to capture global correlations. 











## Get Started

1. `pip install -r requirements.txt`

2. Data. All the six benchmark datasets can be obtained from [Google Drive](https://drive.google.com/file/d/1CC4ZrUD4EKncndzgy5PSTzOPSqcuyqqj/view?usp=sharing) or [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/b8f4a78a39874ac9893e/?dl=1).

3. Reproducibility. We provide the experiment scripts of all benchmarks under the folder `./scripts`. You can reproduce the experiments results by:

```

bash ./scipts/ETTm.sh

bash ./scipts/ETTh.sh

bash ./scipts/ECL.sh

bash ./scipts/Exchange.sh

bash ./scipts/Traffic.sh

bash ./scipts/WTH.sh

bash ./scipts/ILI.sh

```

## Experiments

### Main Results

#### Multivariate results

![arch](./img/multi_results.png)

#### Univariate results

![arch](./img/uni_results.png)

### Model Analysis

#### Local-global vs. self-attetion, Auto-correlation

![arch](./img/local-global-analysis1.png)

![arch](./img/local-global-analysis2.png)

### Visualization

Visualization of learned trend-cyclical part prediction and seasonal part prediction.

![arch](./img/visualization.png)

## Contact

If you have any questions, please contact [email protected]. Welcome to discuss together.

## Citation

If you find this repo useful, please cite our paper

```

@article{micn,

  title={MICN: Multi-scale Local and Global Context Modeling for Long-term Series Forecasting},

  author={Huiqiang Wang and Jian Peng and Feihu Huang and Jince Wang and Junhui Chen and Yifei Xiao},

  booktitle={International Conference on Learning Representations},

  year={2023}

}

```

## Acknowledgement

We appreciate the following github repos a lot for their valuable code base or datasets:

https://github.com/thuml/Autoformer

https://github.com/zhouhaoyi/Informer2020

https://github.com/zhouhaoyi/ETDataset

https://github.com/laiguokun/multivariate-time-series-data