https://github.com/wzhwzhwzh0921/S-D-Mamba

Code for "Is Mamba Effective for Time Series Forecasting?"
https://github.com/wzhwzhwzh0921/S-D-Mamba

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Code for "Is Mamba Effective for Time Series Forecasting?"

• Host: GitHub
• URL: https://github.com/wzhwzhwzh0921/S-D-Mamba
• Owner: wzhwzhwzh0921
• Created: 2024-03-17T07:46:44.000Z (10 months ago)
• Default Branch: main
• Last Pushed: 2024-04-30T07:42:11.000Z (9 months ago)
• Last Synced: 2024-08-01T04:02:11.546Z (6 months ago)
• Language: Python
• Size: 667 KB
• Stars: 154
• Watchers: 5
• Forks: 19
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• Awesome-state-space-models - Time Series

README

        
# Is Mamba Effective for Time Series Forecasting?



## :loudspeaker: Latest Updates

- **2024 Apr-27** : Updated our paper (v3). [[arXiv]](https://arxiv.org/abs/2403.11144v3) [[PDF]](https://arxiv.org/pdf/2403.11144v3). 

- **2024 Apr-02** : Updated our paper and released the code. You can refer to [[arXiv]](https://arxiv.org/abs/2403.11144) for more details. 

## S-Mamba

![model](assets/S-Mamba.png)

## Contributions :trophy:

- We propose S-Mamba, a Mamba-based model for time series forecasting, which delegates the extraction of inter-variate correlations and temporal dependencies to a bidirectional Mamba block and a Feed-Forward network. 

- We evaluate the performance of S-Mamba, which not only has low GPU memory required and short time for forecasts but also maintains superior performance compared to the representative and state-of-the-art models. 

- We conduct extensive experiments to further delve deeper into Mamba's potential in time series forecasting tasks.

## Getting Start :hourglass_flowing_sand:

### Installation

```bash

pip install -r requirements.txt

```

### Datasets

The datasets can be obtained from [here](https://github.com/wzhwzhwzh0921/S-D-Mamba/releases/download/datasets/S-Mamba_datasets.zip).

### Train and evaluate

```bash

# ECL

bash ./scripts/multivariate_forecasting/ECL/S_Mamba.sh

# Exchange

bash ./scripts/multivariate_forecasting/Exchange/S_Mamba.sh

# Traffic

bash ./scripts/multivariate_forecasting/Traffic/S_Mamba.sh

# Weather

bash ./scripts/multivariate_forecasting/Weather/S_Mamba.sh

# Solar-Energy

bash ./scripts/multivariate_forecasting/SolarEnergy/S_Mamba.sh

# PEMS

bash ./scripts/multivariate_forecasting/PEMS/S_Mamba_03.sh

bash ./scripts/multivariate_forecasting/PEMS/S_Mamba_04.sh

bash ./scripts/multivariate_forecasting/PEMS/S_Mamba_07.sh

bash ./scripts/multivariate_forecasting/PEMS/S_Mamba_08.sh

# ETT

bash ./scripts/multivariate_forecasting/ETT/S_Mamba_ETTm1.sh

bash ./scripts/multivariate_forecasting/ETT/S_Mamba_ETTm2.sh

bash ./scripts/multivariate_forecasting/ETT/S_Mamba_ETTh1.sh

bash ./scripts/multivariate_forecasting/ETT/S_Mamba_ETTh2.sh

```

## Acknowledgement :pray:

We are grateful for the following awesome projects when implementing S-Mamba:

- [iTransformer](https://github.com/thuml/iTransformer)

- [Mamba](https://github.com/state-spaces/mamba)

## Citation  

If you find our work  useful in your research, please consider citing us:

```

@article{wang2024mamba,

  title={Is Mamba Effective for Time Series Forecasting?},

  author={Wang, Zihan and Kong, Fanheng and Feng, Shi and Wang, Ming and Zhao, Han and Wang, Daling and Zhang, Yifei},

  journal={arXiv preprint arXiv:2403.11144},

  year={2024}

}

```