FreDN: Frequency Domain Decomposition Network for Long-term Time Series Forecasting

Official implementation for AAAI 2026 paper.

Overview

FreDN is a novel deep learning model for long-term time series forecasting that leverages frequency domain decomposition and dual-branch prediction for seasonal and trend components.

Requirements

Environment

• Python 3.9+
• PyTorch 2.7.1 (CUDA 12.6)
• CUDA-compatible GPU (optional, CPU mode supported)

Installation

# Create conda environment
conda create -n fredn python=3.9
conda activate fredn

# Install dependencies
pip install -r requirements.txt

Dataset Preparation

The code supports the following datasets:

• ETT (Electricity Transformer Temperature): ETTh1, ETTh2, ETTm1, ETTm2
• Electricity: ECL dataset
• Traffic: Traffic dataset
• Weather: Weather dataset

Place datasets in ./dataset/ directory:

dataset/
├── ETT-small/
│   ├── ETTh1.csv
│   ├── ETTh2.csv
│   ├── ETTm1.csv
│   └── ETTm2.csv
├── electricity/
│   └── electricity.csv
├── traffic/
│   └── traffic.csv
└── weather/
    └── weather.csv

Quick Start

Training

Run experiments using provided scripts:

# ETTh1 dataset
bash scripts/ETTh1.sh

# Electricity dataset
bash scripts/ECL.sh

# Traffic dataset
bash scripts/traffic.sh

# Weather dataset
bash scripts/weather.sh

Citation

If you find this work helpful, please cite:

@article{an2026fredn,
  title   = {FreDN: Spectral Disentanglement for Time Series Forecasting via Learnable Frequency Decomposition},
  author  = {An, Zhongde and You, Jinhong and Li, Jiyanglin and Tang, Yiming and Li, Wen and Du, Heming and Du, Shouguo},
  journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
  volume  = {40},
  number  = {24},
  pages   = {19623--19631},
  year    = {2026},
  month   = mar,
  doi     = {10.1609/aaai.v40i24.39042},
  url     = {https://ojs.aaai.org/index.php/AAAI/article/view/39042}
}

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgements

This codebase is built upon the [Time-Series-Library](https://github.com/thuml/Time-Series-Library) framework.

## Contact

For questions or issues, please open an issue or contact: [2023213372@stu.sufe.edu.cn]