Original scientific paper

https://doi.org/10.20532/cit.2018.1004217

A Weighted DTW Approach for Similarity Matching over Uncertain Time Series

Liangli Zuo ; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China
Li Yan orcid.org/0000-0002-1881-3128 ; College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China

Abstract

To measure uncertain time series similarity effectively and efficiently, in this paper, we propose a weighted DTW distance-based approach for uncertain time series with the expected distance. We introduce a weight function to assign weights to a reference point and a testing point. With this function and the WDTW, the accuracy of calculating uncertain time series similarity can be improved. Also, to reduce the storage space and time-consuming, we extend the lower bound function LB_Keogh for DTW into ULB_Keogh for our approach.

Keywords

uncertain time series; similarity matching; dynamic time warping (DTW); weighted DTW

Hrčak ID:

213690

URI

https://hrcak.srce.hr/213690

Publication date:

18.12.2018.

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