{"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/explainable-time-series-tweaking-via","title":"Explainable time series tweaking via irreversible and reversible temporal transformations","arxiv_id":"1809.05183","date":"2018-09-13","proceeding":null,"authors":["Isak Karlsson","Jonathan Rebane","Panagiotis Papapetrou","Aristides Gionis"],"abstract":"Time series classification has received great attention over the past decade\nwith a wide range of methods focusing on predictive performance by exploiting\nvarious types of temporal features. Nonetheless, little emphasis has been\nplaced on interpretability and explainability. In this paper, we formulate the\nnovel problem of explainable time series tweaking, where, given a time series\nand an opaque classifier that provides a particular classification decision for\nthe time series, we want to find the minimum number of changes to be performed\nto the given time series so that the classifier changes its decision to another\nclass. We show that the problem is NP-hard, and focus on two instantiations of\nthe problem, which we refer to as reversible and irreversible time series\ntweaking. The classifier under investigation is the random shapelet forest\nclassifier. Moreover, we propose two algorithmic solutions for the two problems\nalong with simple optimizations, as well as a baseline solution using the\nnearest neighbor classifier. An extensive experimental evaluation on a variety\nof real datasets demonstrates the usefulness and effectiveness of our problem\nformulation and solutions.","url_abs":"http://arxiv.org/abs/1809.05183v1","url_pdf":"http://arxiv.org/pdf/1809.05183v1.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":"explainable-time-series-tweaking-via","repo_url":"https://github.com/isakkarlsson/tsexplain","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"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"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}