This is the repository of the paper entitled "LightCTS: A Lightweight Framework for Correlated Time Series Forecasting", encompassing the code, datasets, and 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).
To install requirements:
pip3 install -r requirements.txt
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.
| 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 |
- 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}
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.
| 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 |
- 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 can be download from Google Drive. Please replace "args.checkpoint" with the corresponding path in the test code file.
The code might be a little messy. For any question, feel free to contact: ``` Zhichen Lai: zhla@cs.aau.dk ```