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SWIFT: Mapping Sub-series with Wavelet Decomposition Improves Time Series Forecasting

SWIFT 최신버젼 다운로드

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# SWIFT: Mapping Sub-series with Wavelet Decomposition Improves Time Series Forecasting

[![arXiv](https://img.shields.io/badge/arXiv-2501.16178-B31B1B.svg)](https://arxiv.org/abs/2501.16178)
[![License](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/Lancelot-Xie/SWIFT/main/LICENSE)  
[![Python](https://img.shields.io/badge/python-3.9%2B-blue)](https://www.python.org/)

SWIFT is a lightweight and efficient model for **Long-term Time Series Forecasting (LTSF)** that integrates **wavelet decomposition**, **cross-band information fusion**, and **sub-series mapping** techniques to improve forecasting accuracy, especially in resource-constrained environments.

---

## 🚀 Features

- **Wavelet-based Lossless Downsampling**: Uses **Discrete Wavelet Transform (DWT)** for effective time-frequency analysis.  
- **Cross-band Information Fusion**: Learns a **shared representation** across different frequency bands.  
- **Minimalist Model Design**: Employs a **single-layer linear** or **shallow MLP** for sub-series mapping.  
- **Superior Performance**: Achieves **state-of-the-art (SOTA)** results with **only 25%** of the parameters of a standard linear model.  
- **Optimized for Edge Devices**: Suitable for **low-computation environments** with fast inference speed.

---

## 🔧 Installation

Clone the repository and install dependencies:

```bash
git clone https://github.com/LancelotXWX/SWIFT.git
cd SWIFT
pip install -r requirements.txt
```

---

## 📝 Dataset
You can obtain the well pre-processed datasets from [[Google Drive]](https://drive.google.com/drive/folders/13Cg1KYOlzM5C7K8gK8NfC-F3EYxkM3D2?usp=sharing) or [[Baidu Drive]](https://pan.baidu.com/s/1r3KhGd0Q9PJIUZdfEYoymg?pwd=i9iy), in a file named 'all_datasets.zip'. Place the downloaded data in the folder`./dataset`.

---

## 🔥 Run

To run the experiment in paper:
```bash
bash scripts/SWIFT/etth1.sh
bash scripts/SWIFT/traffic.sh
```

---

## 📌 Citing SWIFT

If you find SWIFT useful in your research, please cite:

```bibtex
@article{xie2025swift,
  title={SWIFT: Mapping Sub-series with Wavelet Decomposition Improves Time Series Forecasting},
  author={Xie, Wenxuan and Cao, Fanpu},
  journal={arXiv preprint arXiv:2501.16178},
  year={2025}
}
```

SWIFT

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