{"type":"ScholarlyArticle","title":"Multivariate Times Series Classification Using Multichannel CNN","authors":[{"name":"YongKyung Oh","slug":"yongkyung-oh","given":"YongKyung","family":"Oh","affiliation":"Department of Industrial Engineering , Ulsan National Institute of Science and Technology (UNIST)"}],"abstract":"Multivariate time series classification is an important and demanding task in sequence data mining. We focus on the multichannel representation of the time series and its corresponding convolutional neural network (CNN) classifier. The proposed method transforms multivariate time series into multichannel analogous image and it is fed into a pretrained multichannel CNN with transfer learning. To verify the efficacy of the proposed method, we compared it with recent deep learning-based time series classification models on five datasets with small amounts of training data. The results indicate that the proposed method provides improved performance on average compared with the other methods when incorporated with transfer learning.","doi":"10.24963/ijcai.2022/835","url":"https://ijcai.org/proceedings/2022/835","pdf":"https://ijcai.org/proceedings/2022/0835.pdf","markdown":"https://ijcai.org/proceedings/2022/835.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/835","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Doctoral Consortium","track":"Doctoral Consortium","number":835,"pages":{"first":5865,"last":5866},"keywords":["Machine Learning (ML): General"],"videos":[],"references":[{"position":1,"raw":"[Bagnall et al., 2018] Anthony Bagnall, Hoang Anh Dau, Jason Lines, Michael Flynn, James Large, Aaron Bostrom, Paul Southam, and Eamonn Keogh. 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Multichannel convolution neural network for gas mixture classification","authors":"Kim Oh","year":"2021","venue":"2021 International Conference on Data Mining Workshops (ICDMW)","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Wang et al., 2017] Zhiguang Wang, Weizhong Yan, and Tim Oates. Time series classification from scratch with deep neural networks: A strong baseline. In 2017 International joint con- ference on neural networks (IJCNN), pages 1578-1585. IEEE, 2017.","title":"Time series classification from scratch with deep neural networks: A strong baseline","authors":"Zhiguang Wang, Weizhong Yan, Tim Oates","year":"2017","venue":"2017 International joint conference on neural networks (IJCNN)","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Zhao et al., 2017] Bendong Zhao, Huanzhang Lu, Shangfeng Chen, Junliang Liu, and Dongya Wu. Convolutional neural net- works for time series classification. 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