This is the original pytorch implementation of AutoCTS in the following paper: AutoCTS: Automated Correlated Time Series Forecasting.
This code is based on the implementation of PC-Darts.
- python 3
- see
requirements.txt
AutoCTS is implemented on several public correlated time series forecasting datasets.
-
METR-LA and PEMS-BAY from Google Drive or Baidu Yun links provided by DCRNN
-
PEMS03, PEMS04, PEMS07 and PEMS08 from STSGCN (AAAI-20). Download the data STSGCN_data.tar.gz with password:
p72z
and uncompress data file usingtar -zxvf data.tar.gz
To run AutoCTS on the PEMS03 dataset, you only need to download the PEMS03_data.csv and put it into the data/pems/PEMS03 folder. -
Solar-Energy and Electricity datasets from https://github.com/laiguokun/multivariate-time-series-data. Uncompress them and move them to the data folder.
CUDA_VISIBLE_DEVICES=0 python3 train_search.py
If you use AutoCTS for your research, please cite the following paper.
@article{wu2022autocts, title={AutoCTS: Automated Correlated Time Series Forecasting}, author={Xinle Wu and Dalin Zhang and Chenjuan Guo and Chaoyang He and Bin Yang and Christian S. Jensen}, year={2022}, pages={971--983} journal={Proceedings of the VLDB Endowment}, volume={4} }