{"type":"ScholarlyArticle","title":"T-SMOTE: Temporal-oriented Synthetic Minority Oversampling Technique for Imbalanced Time Series Classification","authors":[{"name":"Pu Zhao","slug":"pu-zhao","given":"Pu","family":"Zhao","affiliation":"Microsoft Research"},{"name":"Chuan Luo","slug":"chuan-luo","given":"Chuan","family":"Luo","affiliation":"Microsoft Research"},{"name":"Bo Qiao","slug":"bo-qiao","given":"Bo","family":"Qiao","affiliation":"Microsoft Research"},{"name":"Lu Wang","slug":"lu-wang","given":"Lu","family":"Wang","affiliation":"Microsoft Research"},{"name":"Saravan Rajmohan","slug":"saravan-rajmohan","given":"Saravan","family":"Rajmohan","affiliation":"Microsoft 365"},{"name":"Qingwei Lin","slug":"qingwei-lin","given":"Qingwei","family":"Lin","affiliation":"Microsoft Research"},{"name":"Dongmei Zhang","slug":"dongmei-zhang","given":"Dongmei","family":"Zhang","affiliation":"Microsoft Research"}],"abstract":"Time series classification is a popular and important topic in machine learning, and it suffers from the class imbalance problem in many real-world applications. In this paper, to address the class imbalance problem, we propose a novel and practical oversampling method named T-SMOTE, which can make full use of the temporal information of time-series data. In particular, for each sample of minority class, T-SMOTE generates multiple samples that are close to class border. Then, based on those samples near class border, T-SMOTE synthesizes more samples. Finally, a weighted sampling method is called on both generated samples near class border and synthetic samples. Extensive experiments on a diverse set of both univariate and multivariate time-series datasets demonstrate that T-SMOTE consistently outperforms the current state-of-the-art methods on imbalanced time series classification. More encouragingly, our empirical evaluations show that T-SMOTE performs better in the scenario of early prediction, an important application scenario in industry, which indicates that T-SMOTE could bring benefits in practice.","doi":"10.24963/ijcai.2022/334","url":"https://ijcai.org/proceedings/2022/334","pdf":"https://ijcai.org/proceedings/2022/0334.pdf","markdown":"https://ijcai.org/proceedings/2022/334.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/334","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":"Data Mining","track":"Main Track","number":334,"pages":{"first":2406,"last":2412},"keywords":["Data Mining: Class Imbalance and Unequal Cost","Data Mining: Mining Spatial and/or Temporal Data"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38987085","length_sec":85,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985228","length_sec":477,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Barua et al., 2014] Sukarna Barua, Md. 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