Recovering DTW distance between noise superposed NHPP

Chang, Y; Li, Z; Zhang, B; Luo, L; Sowmya, A; Wang, Y; Chen, F

Recovering DTW distance between noise superposed NHPP

Chang, Y Li, Z

Zhang, B Luo, L Sowmya, A Wang, Y Chen, F

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11440 LNAI pp. 229 - 241
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Chang, Y	en_US
dc.contributor.author	Li, Z https://orcid.org/0000-0002-0784-157X	en_US
dc.contributor.author	Zhang, B	en_US
dc.contributor.author	Luo, L	en_US
dc.contributor.author	Sowmya, A	en_US
dc.contributor.author	Wang, Y	en_US
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729	en_US
dc.date.issued	2019-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11440 LNAI pp. 229 - 241	en_US
dc.identifier.isbn	9783030161446	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/139060
dc.description.abstract	© Springer Nature Switzerland AG 2019. Unmarked event data is increasingly popular in temporal modeling, containing only the timestamp of each event occurrence without specifying the class or description of the events. A sequence of event is usually modeled as the realization from a latent intensity series. When the intensity varies, the events follow the Non-Homogeneous Poisson Process (NHPP). To analyze a sequence of such kind of events, an important task is to measure the similarity between two sequences based on their intensities. To avoid the difficulties of estimating the latent intensities, we measure the similarity using timestamps by Dynamic Time Warping (DTW), which can also resolve the issue that observations between two sequences are not aligned in time. Furthermore, real event data always has superposed noise, e.g. when comparing the purchase behaviour of two customers, we can be mislead if one customer visits market more often because of some occasional shopping events. We shall recover the DTW distance between two noise-superposed NHPP sequences to evaluate the similarity between them. We proposed two strategies, which are removing noise events on all possibilities before calculating the DTW distance, and integrating the noise removal into the DTW calculation in dynamic programming. We compare empirical performance of all the methods and quantitatively show that the proposed methods can recover the DTW distance effectively and efficiently.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-030-16145-3_18	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Recovering DTW distance between noise superposed NHPP	en_US
dc.type	Conference Proceeding
utslib.citation.volume	11440 LNAI	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	11440 LNAI	en_US

Abstract:

© Springer Nature Switzerland AG 2019. Unmarked event data is increasingly popular in temporal modeling, containing only the timestamp of each event occurrence without specifying the class or description of the events. A sequence of event is usually modeled as the realization from a latent intensity series. When the intensity varies, the events follow the Non-Homogeneous Poisson Process (NHPP). To analyze a sequence of such kind of events, an important task is to measure the similarity between two sequences based on their intensities. To avoid the difficulties of estimating the latent intensities, we measure the similarity using timestamps by Dynamic Time Warping (DTW), which can also resolve the issue that observations between two sequences are not aligned in time. Furthermore, real event data always has superposed noise, e.g. when comparing the purchase behaviour of two customers, we can be mislead if one customer visits market more often because of some occasional shopping events. We shall recover the DTW distance between two noise-superposed NHPP sequences to evaluate the similarity between them. We proposed two strategies, which are removing noise events on all possibilities before calculating the DTW distance, and integrating the noise removal into the DTW calculation in dynamic programming. We compare empirical performance of all the methods and quantitatively show that the proposed methods can recover the DTW distance effectively and efficiently.

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http://hdl.handle.net/10453/139060