{"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/pasta-neural-architecture-search-for-anomaly","title":"PASTA: Neural Architecture Search for Anomaly Detection in Multivariate Time Series","arxiv_id":null,"date":"2024-12-09","proceeding":"IEEE Transactions on Emerging Topics in Computational Intelligence 2024 12","authors":["Patara Trirat","Jae-Gil Lee"],"abstract":"Time-series anomaly detection uncovers rare errors or intriguing events of interest that significantly deviate from normal patterns. In order to precisely detect anomalies, a detector needs to capture intricate underlying temporal dynamics of a time series, often in multiple scales. Thus, a fixed-designed neural network may not be optimal for capturing such complex dynamics as different time-series data require different learning processes to reflect their unique characteristics. This paper proposes a Prediction-based neural Architecture Search for Time series Anomaly detection framework, dubbed PASTA. Unlike previous work, besides searching for a connection between operations, we design a novel search space to search for optimal connections in the temporal dimension among recurrent cells within/between each layer, i.e., temporal connectivity, and encode them via multi-level configuration encoding networks. Experimental results from both real-world and synthetic benchmarks show that the discovered architectures by PASTA outperform the second-best state-of-the-art baseline by around 13.6% in the enhanced time-series aware F1 score on average, confirming that the design of temporal connectivity is critical for time-series anomaly detection.","url_abs":"https://ieeexplore.ieee.org/document/10787122","url_pdf":"https://itouchz.github.io/files/PASTA_TETCI.pdf","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":"pasta-neural-architecture-search-for-anomaly","repo_url":"https://github.com/kaist-dmlab/PASTA","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"architecture-search","task_name":"Neural Architecture Search"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-anomaly-detection","task_name":"Time Series Anomaly Detection"}],"methods":[{"method_slug":"aware","method_name":"AWARE"}],"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}