{"type":"ScholarlyArticle","title":"Toward Unsupervised Activity Discovery Using Multi-Dimensional Motif Detection in Time Series","authors":[{"name":"Alireza Vahdatpour","slug":"alireza-vahdatpour","given":"Alireza","family":"Vahdatpour","affiliation":""},{"name":"Navid Amini","slug":"navid-amini","given":"Navid","family":"Amini","affiliation":""},{"name":"Majid Sarrafzadeh","slug":"majid-sarrafzadeh","given":"Majid","family":"Sarrafzadeh","affiliation":""}],"abstract":"This paper addresses the problem of activity and event discovery in multi dimensional time series data by proposing a novel method for locating multi dimensional motifs in time series. While recent work has been done in finding single dimensional and multi dimensional motifs in time series, we address motifs in general case, where the elements of multi dimensional motifs have temporal, length, and frequency variations. The proposed method is validated by synthetic data, and empirical evaluation has been done on several wearable systems that are used by real subjects.Alireza Vahdatpour, Navid Amini, Majid Sarrafzadeh","doi":null,"url":"https://ijcai.org/proceedings/2009/212","pdf":"https://ijcai.org/proceedings/2009/0212.pdf","markdown":"https://ijcai.org/proceedings/2009/212.md","bibtex":"https://ijcai.org/proceedings/2009/bibtex/212","volume":{"slug":"2009","title":"Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2009","url":"https://ijcai.org/proceedings/2009/","year":2009,"location":"Pasadena, California, USA","dates":"","editors":"Hiroaki Kitano","isbn":"","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":212,"pages":{"first":1261,"last":1265},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}