NDLI: A shape-based similarity measure for time series data with ensemble learning

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A shape-based similarity measure for time series data with ensemble learning

Content Provider	SpringerLink
Author	Nakamura, Tetsuya Taki, Keishi miya, Hiroki Seki, Kazuhiro Uehara, Kuniaki
Copyright Year	2012
Abstract	This paper introduces a shape-based similarity measure, called the angular metric for shape similarity (AMSS), for time series data. Unlike most similarity or dissimilarity measures, AMSS is based not on individual data points of a time series but on vectors equivalently representing it. AMSS treats a time series as a vector sequence to focus on the shape of the data and compares data shapes by employing a variant of cosine similarity. AMSS is, by design, expected to be robust to time and amplitude shifting and scaling, but sensitive to short-term oscillations. To deal with the potential drawback, ensemble learning is adopted, which integrates data smoothing when AMSS is used for classification. Evaluative experiments reveal distinct properties of AMSS and its effectiveness when applied in the ensemble framework as compared to existing measures.
Starting Page	535
Ending Page	548
Page Count	14
File Format	PDF
ISSN	14337541
Journal	Pattern Analysis & Applications
Volume Number	16
Issue Number	4
e-ISSN	1433755X
Language	English
Publisher	Springer London
Publisher Date	2012-01-12
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Time series analysis Similarity measures Machine learning Pattern Recognition
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Computer Vision and Pattern Recognition

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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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