{"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/learning-time-series-counterfactuals-via","title":"Learning Time Series Counterfactuals via Latent Space Representations","arxiv_id":null,"date":"2021-10-11","proceeding":"International Conference on Discovery Science 2021 10","authors":["Zhendong Wang","Isak Samsten","Rami Mochaourab","Panagiotis Papapetrou"],"abstract":"Counterfactual explanations can provide sample-based explanations of features required to modify from the original sample to change the classification result from an undesired state to a desired state; hence it provides interpretability of the model. Previous work of LatentCF presents an algorithm for image data that employs auto-encoder models to directly transform original samples into counterfactuals in a latent space representation. In our paper, we adapt the approach to time series classification and propose an improved algorithm named LatentCF++ which introduces additional constraints in the counterfactual generation process. We conduct an extensive experiment on a total of 40 datasets from the UCR archive, comparing to current state-of-the-art methods. Based on our evaluation metrics, we show that the LatentCF++ framework can with high probability generate valid counterfactuals and achieve …","url_abs":"https://www.researchgate.net/publication/355171843_Learning_Time_Series_Counterfactuals_via_Latent_Space_Representations","url_pdf":"https://www.researchgate.net/publication/355171843_Learning_Time_Series_Counterfactuals_via_Latent_Space_Representations","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":"learning-time-series-counterfactuals-via","repo_url":"https://github.com/zhendong3wang/learning-time-series-counterfactuals","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"counterfactual-explanation","task_name":"Counterfactual Explanation"},{"task_slug":"time-series-1","task_name":"Time Series"},{"task_slug":"time-series-classification","task_name":"Time Series Classification"},{"task_slug":null,"task_name":"counterfactual"},{"task_slug":null,"task_name":"valid"}],"methods":[{"method_slug":"counterfactuals","method_name":"Counterfactuals"}],"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}