A General Approach for Supporting Time Series Matching Using Multiple-Warped Distances

Neamtu, Rodica and Ahsan, Ramoza and Nguyen, Cuong Dinh Tri and Lovering, Charles and Rundensteiner, Elke A. and Sárközy, Gábor (2022) A General Approach for Supporting Time Series Matching Using Multiple-Warped Distances. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 34 (4). pp. 1516-1529. ISSN 1041-4347


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Time series are generated at an unprecedented rate in domains ranging from finance, medicine to education. Collections composed of heterogeneous, variable-length and misaligned times series are best explored using a plethora of dynamic time warping distances. However, the computational costs of using such elastic distances result in unacceptable response times. We thus design the first practical solution for the efficient GENeral EXploration of time series leveraging multiple warped distances. GENEX pre-processes time series data in metric point-wise distance spaces, while providing bounds for the accuracy of corresponding analytics derived in non-metric warped distance spaces. Our empirical evaluation on 66 benchmark datasets provides a comparative study of the accuracy and response times of diverse warped distances. We show that GENEX is a versatile yet highly efficient solution for processing expensive-to-compute warped distances over large datasets, with response times 3 to 5 orders of magnitude faster than state-of-art systems.

Item Type: Article
Additional Information: Worcester Polytechnic Institute, Worcester, MA, United States Brown University, Providence, RI, United States Export Date: 16 February 2023 CODEN: ITKEE Correspondence Address: Neamtu, R.; Worcester Polytechnic InstituteUnited States; email:
Uncontrolled Keywords: Time Factors; dynamic programming; data mining; time series analysis; Complexity theory; Dynamic time warping; Extraterrestrial measurements; time series mining; similarity exploration;
Subjects: Q Science / természettudomány > QA Mathematics / matematika
Depositing User: MTMT SWORD
Date Deposited: 17 Mar 2023 08:24
Last Modified: 17 Mar 2023 08:24

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