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Forecasting with Sample Convolution and Interaction



pytorch
cure

This is the original PyTorch implementation of the following work: Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction. If you find this repository useful for your work, please consider citing it as follows:

@article{liu2021SCINet,
  title={Time Series is a Special Sequence: Forecasting with Sample Convolution and Interaction},
  author={Liu, Minhao and Zeng, Ailing and Lai, Qiuxia and Xu, Qiang},
  journal={arXiv preprint arXiv:2106.09305},
  year={2021}
}

traffic
electric
Solar Energy
finance

  • Provide all training logs.

here. To prepare all dataset at one time, you can just run:

ett
pems
financial

The data directory structure is shown as follows.

./
└── datasets/
    ├── ETT-data
    │   ├── ETTh1.csv
    │   ├── ETTh2.csv
    │   └── ETTm1.csv
    ├── financial
    │   ├── electricity.txt
    │   ├── exchange_rate.txt
    │   ├── solar_AL.txt
    │   └── traffic.txt
    └── PEMS
        ├── PEMS03.npz
        ├── PEMS04.npz
        ├── PEMS07.npz
        └── PEMS08.npz

here in details. You can check the hyperparameters, training loss and test results for each epoch in these logs as well.

We follow the same settings of StemGNN for PEMS 03, 04, 07, 08 datasets, MTGNN for Solar, electricity, traffic, financial datasets, Informer for ETTH1, ETTH2, ETTM1 datasets. The detailed training commands are given as follows.

(The formula might not be shown in the darkmode Github)

Contact

If you have any questions, feel free to contact us or post github issues. Pull requests are highly welcomed!

View Github


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