{"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/memory-efficient-centroid-decomposition-for","title":"Memory-Efficient Centroid Decomposition for Long Time Series","arxiv_id":null,"date":"2017-04-01","proceeding":"IEEE 30th International Conference on Data Engineering (ICDE) 2017 4","authors":["Mourad Khayati","Michael Böhlen","Johann Gamper"],"abstract":"Real-world applications that deal with time series data often rely on matrix decomposition techniques, such as the Singular Value Decomposition (SVD). The Centroid Decomposition (CD) approximates the Singular Value Decomposition, but does not scale to long time series because of the quadratic space complexity of the sign vector computation.\r\n\r\nIn this paper, we propose a greedy algorithm, termed Scalable Sign Vector (SSV), to efficiently determine sign vectors for CD applications with long time series, i.e., where the number of rows (observations) is much larger than the number of columns (time series). The SSV algorithm starts with a sign vector consisting of only 1s and iteratively changes the sign of the element that maximizes the benefit. The space complexity of the SSV algorithm is linear in the length of the time series. We provide proofs for the scalability, the termination, and the correctness of the SSV algorithm. Experiments with real-world hydrological time series and data sets from the UCR repository validate the analytical results and show the scalability of SSV.","url_abs":"https://exascale.info/assets/pdf/khayati_ICDE14.pdf","url_pdf":"https://exascale.info/assets/pdf/khayati_ICDE14.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":"memory-efficient-centroid-decomposition-for","repo_url":"https://github.com/eXascaleInfolab/cdrec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}