{"type":"ScholarlyArticle","title":"Uncertain Time Series Classification","authors":[{"name":"Michael Franklin Mbouopda","slug":"michael-franklin-mbouopda","given":"Michael Franklin","family":"Mbouopda","affiliation":""}],"abstract":"Time series analysis has gained a lot of interest during the last decade with diverse applications in a large range of domains such as medicine, physic, and industry. The field of time series classification has been particularly active recently with the development of more and more efficient methods. However, the existing methods assume that the input time series is free of uncertainty. However, there are applications in which uncertainty is so important that it can not be neglected. This project aims to build efficient, robust, and interpretable classification methods for uncertain time series.","doi":"10.24963/ijcai.2021/683","url":"https://ijcai.org/proceedings/2021/683","pdf":"https://ijcai.org/proceedings/2021/0683.pdf","markdown":"https://ijcai.org/proceedings/2021/683.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/683","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":683,"pages":{"first":4903,"last":4904},"keywords":["Machine Learning: Time-series; Data Streams","Machine Learning: Classification","Machine Learning: Explainable/Interpretable Machine Learning"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}