README.md

September 18, 2025 · View on GitHub

This is an official implementation of [DPANet: Dual Pyramid Attention Network for Multivariate Time Series Forecasting]. image

Prerequisites

Ensure you are using Python 3.9 and install the necessary dependencies by running:

pip install -r requirements.txt

1. Data Preparation

Download data from AutoFormer. Put all data into a seperate folder ./dataset and make sure it has the following structure:

dataset
├── electricity.csv
├── ETTh1.csv
│── ETTh2.csv
│── ETTm1.csv
│── ETTm2.csv
├── traffic.csv
└── weather.csv

2. Training

The training scripts for all datasets are located in the ./scripts directory.

To train a model using the ETTh1 dataset:

  1. Navigate to the repository's root directory.
  2. Execute the following command: 
    sh ./scripts/ETTh1.sh

Upon completion of the training:

  • The trained model will be saved in the ./checkpoints directory.
  • Visualization outputs can be found in ./test_results.
  • Numerical results in .npy format are located in ./results.
  • A summary of the quantitative metrics is available in ./results.txt.

Citation

If you find this repo useful, please cite our paper as follows:

DPANet: Dual Pyramid Attention Network for Multivariate Time Series Forecasting

Contact

If you have any questions, please contact us or submit an issue.

Acknowledgement

We appreciate the following repo for their code and dataset: