{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/an-effective-wgan-based-anomaly-detection","title":"An Effective WGAN-based Anomaly Detection Model for loT Multivariate Time Series Published on Pacific-Asia Conference on Knowledge Discovery and Data Mining","arxiv_id":null,"date":"2023-05-27","proceeding":"Pacific-Asia Conference on Knowledge Discovery and Data Mining 2023 5","authors":["Sibo Qi","Juan Chen","Peng Chen","Peian Wen","Wenyu Shan","Ling Xiong"],"abstract":"This paper studies an effective unsupervised deep learning model for multivariate time series anomaly detection. Since multivariate time series usually have problems of insufficient labeling and highly-complex temporal correlation, effectively detecting anomalies in multivariate time series data is particularly challenging. To solve this problem, we propose a model named Wasserstein-GAN with gradient Penalty and effective Scoring (WPS). In this model, Wasserstein Distance with Gradient Penalty helps to capture the data regularities between generator output and real data, thus improving the training stability. Meanwhile, an effective scoring function that consists of reconstruction error, discrimination error, and prediction error is designed to evaluate the accuracy of the abnormal prediction and recall. The experimental results show that compared with the suboptimal baseline model, our proposed WPS obtains 17.68% and 10.41% improvement in prediction precision and F1 score, respectively.","url_abs":"https://link.springer.com/chapter/10.1007/978-3-031-33374-3_7","url_pdf":"https://link.springer.com/chapter/10.1007/978-3-031-33374-3_7","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"an-effective-wgan-based-anomaly-detection","repo_url":"https://github.com/yunchispk/WPS_PAKDD","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-anomaly-detection","task_name":"Time Series Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}