Skip to content

Latest commit

 

History

History
98 lines (65 loc) · 3.92 KB

README.md

File metadata and controls

98 lines (65 loc) · 3.92 KB
Raw

LightCTS: A Lightweight Framework for Correlated Time Series Forecasting

This is the repository of the paper entitled "LightCTS: A Lightweight Framework for Correlated Time Series Forecasting", encompassing the code, datasets, and supplemental material.

Supplemental Material

Detailed time and space complexity analysis and other groups of experimental results can be found at the Supplemental Material (downloading to local pdf viewer is recommended for better readability).



Code and Datasets

Requirements

To install requirements:

pip3 install -r requirements.txt

Multi-step Forecasting

Datasets

LightCTS is implemented on four public multi-step correlated time series forecasting datasets.

  • PEMS04, PEMS08, METR-LA, and PEMS-BAY can be downloaded in Google Drive. Please move them to the corresponding data folders.

Baselines

Model Conference Year Link
DCRNN ICLR 2018 https://openreview.net/pdf?id=SJiHXGWAZ
GWNET IJCAI 2019 https://dl.acm.org/doi/10.5555/3367243.3367303
AGCRN NeurIPS 2020 https://dl.acm.org/doi/abs/10.5555/3495724.3497218
MTGNN KDD 2020 https://dl.acm.org/doi/abs/10.1145/3394486.3403118
AUTOCTS VLDB 2021 https://dl.acm.org/doi/10.14778/3503585.3503604
EnhanceNet ICDE 2021 https://ieeexplore.ieee.org/abstract/document/9458855
FOGS IJCAI 2022 https://www.ijcai.org/proceedings/2022/545

Model Training and Testing

  • PEMS04/PEMS08
cd Multi-step/Traffic Flow/{dataset_name}/
python train_{dataset_name_in_lowercase}.py
python test_{dataset_name_in_lowercase}.py -checkpoint {path_to_the_checkpoint_file}
  • METR-LA/PEMS-BAY
cd Multi-step/Traffic Speed/{dataset_name}/
python train_{dataset_name_in_lowercase}.py
python test_{dataset_name_in_lowercase}.py -checkpoint {path_to_the_checkpoint_file}

Single-step Forecasting

Datasets

LightCTS is implemented on two public single-step correlated time series forecasting datasets.

  • Solar and Electricity datasets can be downloaded in Google Drive. Please move them to the corresponding data folders.

Baselines

Model Conference Year Link
DSANet CIKM 2019 https://dl.acm.org/doi/abs/10.1145/3357384.3358132
MTGNN KDD 2020 https://dl.acm.org/doi/abs/10.1145/3394486.3403118
AUTOCTS VLDB 2021 https://dl.acm.org/doi/10.14778/3503585.3503604
MAGNN arXiv 2022 https://arxiv.org/abs/2201.04828

Model Training and Testing

  • Solar-Energy/Electricity
cd Single-step/{dataset_name}/
python train_{dataset_name_in_lowercase}.py
python test_{dataset_name_in_lowercase}.py -checkpoint {path_to_the_checkpoint_file}

Pre-trained checkpoint files

Pre-trained checkpoint files can be download from Google Drive. Please replace "args.checkpoint" with the corresponding path in the test code file.

Contact

The code might be a little messy. For any question, feel free to contact: ``` Zhichen Lai: zhla@cs.aau.dk ```