{"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/time-series-cluster-kernel-for-learning","title":"Time Series Cluster Kernel for Learning Similarities between Multivariate Time Series with Missing Data","arxiv_id":"1704.00794","date":"2017-04-03","proceeding":null,"authors":["Karl Øyvind Mikalsen","Filippo Maria Bianchi","Cristina Soguero-Ruiz","Robert Jenssen"],"abstract":"Similarity-based approaches represent a promising direction for time series\nanalysis. However, many such methods rely on parameter tuning, and some have\nshortcomings if the time series are multivariate (MTS), due to dependencies\nbetween attributes, or the time series contain missing data. In this paper, we\naddress these challenges within the powerful context of kernel methods by\nproposing the robust \\emph{time series cluster kernel} (TCK). The approach\ntaken leverages the missing data handling properties of Gaussian mixture models\n(GMM) augmented with informative prior distributions. An ensemble learning\napproach is exploited to ensure robustness to parameters by combining the\nclustering results of many GMM to form the final kernel.\n  We evaluate the TCK on synthetic and real data and compare to other\nstate-of-the-art techniques. The experimental results demonstrate that the TCK\nis robust to parameter choices, provides competitive results for MTS without\nmissing data and outstanding results for missing data.","url_abs":"http://arxiv.org/abs/1704.00794v2","url_pdf":"http://arxiv.org/pdf/1704.00794v2.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":"time-series-cluster-kernel-for-learning","repo_url":"https://github.com/FilippoMB/TCK_AE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"ensemble-learning","task_name":"Ensemble Learning"},{"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":"https://app.syntology.ai/?focus=1704.00794","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}