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Code for the CIKM 2019 paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting".

Python 100.00%

dsanet's Introduction

Dual Self-Attention Network for Multivariate Time Series Forecasting

This project is the PyTorch implementation of the paper "DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting", in which we propose a dual self-attention network (DSANet) for multivariate time series forecasting. The network architecture is illustrated in the following figure, and more details about the effect of each component can be found in the paper.

Requirements

Python 3.5 or above
PyTorch 1.1 or above
pytorch-lightning

How to run

You need to prepare the dataset first. Check here.

# clone project
git clone https://github.com/bighuang624/DSANet.git

# install dependencies
cd DSANet
pip install requirements.txt

# run
python single_cpu_trainer.py --data_name {data_name} --n_multiv {n_multiv}

Notice: At present, we find that there are some bugs (presumably some problems left by the old version of pytorch-lightning) that make our code unable to run correctly on GPUs. You can currently run the code on the CPU as above.

Citation

If our code is helpful for your research, please cite our paper:

@inproceedings{Huang2019DSANet,
  author = {Huang, Siteng and Wang, Donglin and Wu, Xuehan and Tang, Ao},
  title = {DSANet: Dual Self-Attention Network for Multivariate Time Series Forecasting},
  booktitle = {The 28th ACM International Conference on Information and Knowledge Management (CIKM 2019)},
  month = {November},
  year = {2019},
  address = {Beijing, China}
}