This is an official implementation of [MAFNet: Multi-scale Active Fusion Network for Long-Term Time Series Forecasting].

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:

MAFNet: Multi-scale Active Fusion Network for Long-Term 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:

• https://github.com/thuml/Time-Series-Library
• https://github.com/thuml/Autoformer