{"type":"ScholarlyArticle","title":"A Quantum-inspired Entropic Kernel for Multiple Financial Time Series Analysis","authors":[{"name":"Lu Bai","slug":"lu-bai","given":"Lu","family":"Bai","affiliation":"Central University of Finance and Economics , Beijing , China; China Center for Fintech Studies , Central University of Finance and Economics , Beijing , China"},{"name":"Lixin Cui","slug":"lixin-cui","given":"Lixin","family":"Cui","affiliation":"Central University of Finance and Economics , Beijing , China; China Center for Fintech Studies , Central University of Finance and Economics , Beijing , China"},{"name":"Yue Wang","slug":"yue-wang","given":"Yue","family":"Wang","affiliation":"Central University of Finance and Economics , Beijing , China; State Key Laboratory of Cognitive Intelligence , iFLYTEK , Hefei , China"},{"name":"Yuhang Jiao","slug":"yuhang-jiao","given":"Yuhang","family":"Jiao","affiliation":"Central University of Finance and Economics , Beijing , China"},{"name":"Edwin R. Hancock","slug":"edwin-r-hancock","given":"Edwin R.","family":"Hancock","affiliation":"Department of Computer Science , University of York , York , UK"}],"abstract":"Network representations are powerful tools for the analysis of time-varying financial complex systems consisting of multiple co-evolving financial time series, e.g., stock prices, etc. In this work, we develop a new kernel-based similarity measure between dynamic time-varying financial networks. Our ideas is to transform each original financial network into quantum-based entropy time series and compute the similarity measure based on the classical dynamic time warping framework associated with the entropy time series. The proposed method bridges the gap between graph kernels and the classical dynamic time warping framework for multiple financial time series analysis. Experiments on time-varying networks abstracted from financial time series of New York Stock Exchange (NYSE) database demonstrate that our approach can effectively discriminate the abrupt structural changes in terms of the extreme financial events.","doi":"10.24963/ijcai.2020/614","url":"https://ijcai.org/proceedings/2020/614","pdf":"https://ijcai.org/proceedings/2020/0614.pdf","markdown":"https://ijcai.org/proceedings/2020/614.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/614","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":"Special Track on AI in FinTech","track":"Special Track on AI in FinTech","number":614,"pages":{"first":4453,"last":4460},"keywords":["Foundation for AI in FinTech: Analyzing highdimentional, sequential and evolving financial data","Foundation for AI in FinTech: Analyzing big financial data"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/25545/file_25545.m3u8","length_sec":273,"provider":"hls"}],"references":[{"position":1,"raw":"[Bai and Hancock, 2014] Lu Bai and Edwin R. 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