Code for "Multi-Scale Detection of Anomalous Spatio-Temporal Trajectories in Evolving Trajectory Datasets"
pip install -r requirements.txt
mkdir -p data/<dataset_name>
cd preprocess
python preprocess_<dataset_name>.py
cd ..
mkdir logs models probs
python generate_outliers.py --distance 2 --fraction 0.2 --obeserved_ratio 1.0 --dataset <dataset_name>
distance is used to control the moving distance of outliers, fraction is the fraction of continuous outlier, obeserved_ratio is the ratio of the obeserved part of a trajectory.
python train.py --task train --dataset <dataset_name>
python train.py --task test --distance 2 --fraction 0.2 --obeserved_ratio 1.0 --dataset <dataset_name>
python train_labels.py --dataset <dataset_name>
python train_update.py --update_mode pretrain --dataset <dataset_name> --train_num <train_num>
update_mode contains three modes: pretrain, temporal, rank,
Please kindly cite our work if you find our paper or codes helpful.
@inproceedings{wang2024multi,
title={Multi-Scale Detection of Anomalous Spatio-Temporal Trajectories in Evolving Trajectory Datasets},
author={Wang, Chenhao and Chen, Lisi and Shang, Shuo and Jensen, Christian S and Kalnis, Panos},
booktitle={Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages={2980--2990},
year={2024}
}