{"type":"ScholarlyArticle","title":"Spatio-Temporal Change Detection Using Granger Sequence Pattern","authors":[{"name":"Nat Pavasant","slug":"nat-pavasant","given":"Nat","family":"Pavasant","affiliation":"Graduate School of Information Science And Technology , Osaka University"},{"name":"Masayuki Numao","slug":"masayuki-numao","given":"Masayuki","family":"Numao","affiliation":"The Institute of Scientific and Industrial Research , Osaka University"},{"name":"Ken-ichi Fukui","slug":"ken-ichi-fukui","given":"Ken-ichi","family":"Fukui","affiliation":"The Institute of Scientific and Industrial Research , Osaka University"}],"abstract":"This paper proposed a method to detect changes in causal relations over a multi-dimensional sequence of events. Cluster Sequence Mining algorithm was modified to extract causal relations in the form of g-patterns: a pair of clusters of events that have their occurrence time determined by Granger causality. This paper also proposed the pattern time signature, a probabilistic density function of the cluster sequence occurring at any given time. Synthetic data were used for validation. The result shows that the proposed algorithm can correctly identify the changes in causal relations even under noisy data.","doi":"10.24963/ijcai.2020/741","url":"https://ijcai.org/proceedings/2020/741","pdf":"https://ijcai.org/proceedings/2020/0741.pdf","markdown":"https://ijcai.org/proceedings/2020/741.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/741","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":741,"pages":{"first":5202,"last":5203},"keywords":["Machine Learning: Clustering","Machine Learning: Time-series;Data Streams","Data Mining: Mining Spatial, Temporal Data","Data Mining: Frequent Pattern Mining"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25036/file_25036.m3u8","length_sec":261,"provider":"hls"}],"references":[{"position":1,"raw":"[Atluri et al., 2018] Gowtham Atluri, Anuj Karpatne, and Vipin Kumar. Spatio-temporal data mining: A survey of problems and methods. ACM Computing Surveys, 51(4):1-41, 2018.","title":"Spatio-temporal data mining: A survey of problems and methods","authors":"Gowtham Atluri, Anuj Karpatne, Vipin Kumar","year":"2018","venue":"ACM Computing Surveys","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Fukui et al., 2019] Kenichi Fukui, Yoshiyuki Okada, Kazuki Satoh, and Masayuki Numao. Cluster sequence mining from event sequence data and its application to damage correlation analysis. Knowledge-Based Systems, 179:136-144, 2019.","title":"Cluster sequence mining from event sequence data and its application to damage correlation analysis","authors":"Fukui","year":"2019","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Granger, 1969] C. W. J. Granger. Investigating causal rela- tions by econometric models and cross-spectral methods. Econometrica, 37(3):424-438, 1969.","title":"Investigating causal relations by econometric models and cross-spectral methods","authors":"C W J Granger","year":"1969","venue":"Econometrica","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Kim et al., 2011] Sanggyun Kim, David Putrino, Soumya Ghosh, and Emery N. Brown. A granger causality measure for point process models of ensemble neural spiking activ- ity. PLOS Computational Biology, 7(3):e1001110, 2011.","title":"A granger causality measure for point process models of ensemble neural spiking activity","authors":"Kim","year":"2011","venue":"PLOS Computational Biology","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}