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Is Mamba Effective for Time Series Forecasting?

📢 Latest Updates

  • 2024 Apr-27 : Updated our paper (v3). [arXiv] [PDF].
  • 2024 Apr-02 : Updated our paper and released the code. You can refer to [arXiv] for more details.

S-Mamba

model

Contributions 🏆

  • 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 ⏳

Installation

pip install -r requirements.txt

Datasets

The datasets can be obtained from here.

Train and evaluate

# 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 🙏

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