{"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/interpretable-time-series-classification","title":"Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains","arxiv_id":"1808.04022","date":"2018-08-12","proceeding":null,"authors":["Thach Le Nguyen","Severin Gsponer","Iulia Ilie","Georgiana Ifrim"],"abstract":"The time series classification literature has expanded rapidly over the last\ndecade, with many new classification approaches published each year. The\nresearch focus has mostly been on improving the accuracy and efficiency of\nclassifiers, while their interpretability has been somewhat neglected.\nClassifier interpretability has become a critical constraint for many\napplication domains and the introduction of the 'right to explanation' GDPR EU\nlegislation in May 2018 is likely to further emphasize the importance of\nexplainable learning algorithms. In this work we analyse the state-of-the-art\nfor time series classification, and propose new algorithms that aim to maintain\nthe classifier accuracy and efficiency, but keep interpretability as a key\ndesign constraint. We present new time series classification algorithms that\nadvance the state-of-the-art by implementing the following three key ideas: (1)\nMultiple resolutions of symbolic approximations: we combine symbolic\nrepresentations obtained using different parameters; (2) Multiple domain\nrepresentations: we combine symbolic approximations in time (e.g., SAX) and\nfrequency (e.g., SFA) domains; (3) Efficient navigation of a huge\nsymbolic-words space: we adapt a symbolic sequence classifier named SEQL, to\nmake it work with multiple domain representations (e.g., SAX-SEQL, SFA-SEQL),\nand use its greedy feature selection strategy to effectively filter the best\nfeatures for each representation. We show that a multi-resolution multi-domain\nlinear classifier, SAX-SFA-SEQL, achieves a similar accuracy to the\nstate-of-the-art COTE ensemble, and to a recent deep learning method (FCN), but\nuses a fraction of the time required by either COTE or FCN. We discuss the\naccuracy, efficiency and interpretability of our proposed algorithms. To\nfurther analyse the interpretability aspect of our classifiers, we present a\ncase study on an ecology benchmark.","url_abs":"http://arxiv.org/abs/1808.04022v1","url_pdf":"http://arxiv.org/pdf/1808.04022v1.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":"interpretable-time-series-classification","repo_url":"https://github.com/lnthach/Mr-SEQL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"all","task_name":"All"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"},{"task_slug":"feature-selection","task_name":"feature selection"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fcn","method_name":"FCN"},{"method_slug":"interpretability","method_name":"Interpretability"},{"method_slug":"max-pooling","method_name":"Max Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}