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 ```