MPTSNet

MPTSNet is an implementation of the paper [MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification] (AAAI 2025).

<p align="center"> <img src="docs/poster-compressed.png" width="90%"/> </p>

🛠️ Setup

Repository

Clone the repository:

git clone https://github.com/MUYang99/MPTSNet.git && cd MPTSNet

Installation

Create a conda environment with all dependencies:

conda create --name mptsnet python=3.8
conda activate mptsnet
pip install -r requirements.txt

🚀 Usage

Quick Start

The project supports various UEA time series classification datasets from Time Series Classification Repository. Place your dataset in the dataset/General/ and dataset/UEA/ directory. For quick access to all datasets used in the paper, you can visit this link.

Train MPTSNet:

python train.py

Evaluate MPTSNet:

python eval.py

Model Configuration

The model automatically adapts its parameters based on input dimensions:

• Embedding dimensions are scaled based on input channels
• Periodic patterns are automatically detected using FFT
• Early stopping and learning rate scheduling are implemented for optimal training

Dataset Structure

Structure your dataset as follows:

dataset/General/
└── YOUR_DATASET_NAME
    ├── YOUR_DATASET_NAME_TRAIN.ts
    └── YOUR_DATASET_NAME_TEST.ts

📈 Results

Training results are saved in the results/ directory, including:

• Model checkpoints
• Training logs
• Accuracy metrics

🎓 Citation

If you find it's useful, please cite our paper:

@inproceedings{mu2025mptsnet,
  title={MPTSNet: Integrating Multiscale Periodic Local Patterns and Global Dependencies for Multivariate Time Series Classification},
  author={Mu, Yang and Shahzad, Muhammad and Zhu, Xiao Xiang},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={39},
  number={18},
  pages={19572--19580},
  year={2025}
}

📝 License

This project is licensed under the MIT License.