This repo is the official Pytorch implementation of paper: "Integrating Mamba and Transformer for Long-Short Range Time Series Forecasting".
• We are the first to explore the potential of the integration of Mamba and Transformer in time series.
• We propose to adopt a hybrid architecture Mambaformer to capture long-short range dependencies in time series.
• We conduct a comparative study to demonstrate the superiority of Mambaformer family compared with Mamba and Transformer in long-short range time series forecasting.
- Mambaformer leverages a pre-processing Mamba block and Mambaformer layer without a positional encoding. The architecture is at models/MambaFormer.py, and the layer is at layers/Mamba_Family.py -> AM_Layer.
- Attention-Mamba adopts a Attention-Mamba layer where an attention layer is followed by a Mamba layer with a positional encoding. The architecture is at models/AttMam.py, and the layer is at layers/Mamba_Family.py -> AM_Layer.
- Mamba-Attention adopts a Mamba-Attention layer where a Mamba block layer is followed by an attention layer without a positional encoding. The architecture is at models/MamAtt.py, and the layer is at layers/Mamba_Family.py -> MA_Layer.
- Mamba adopts two Mamba block as a layer. The architecture is at models/Mamba.py, and the layer is at layers/Mamba_Family.py -> Mamba_Layer.
- Transformer is a decoder-only Transformer architecture. the architecture is at models/DecoderOnly.py, and the layer is at layers/Transformer_EncDec.py -> Decoder_wo_cross_Layer.
- python 3.10.13
- torch 1.12.1+cu116
- mamba-ssm 1.2.0.post1
- numpy 1.26.4
- transformers 4.38.2
The installation of mamba-ssm package can refer to https://github.com/state-spaces/mamba.
To get the result of Table 2, run the scripts etth1.sh, electricity.sh, and exchange_rate.sh in a terminal as follows:
./etth1.sh
./electricity.sh
./exchange_rate.sh
We would like to greatly thank the following awesome projects:
Mamba (https://github.com/state-spaces/mamba)
LTSF-Linear (https://github.com/cure-lab/LTSF-Linear)
If you find this repository useful for your work, please consider citing the paper as follows:
@article{xu2024integrating,
title={Integrating Mamba and Transformer for Long-Short Range Time Series Forecasting},
author={Xu, Xiongxiao and Liang, Yueqing and Huang, Baixiang and Lan, Zhiling and Shu, Kai},
journal={arXiv preprint arXiv:2404.14757},
year={2024}
}