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Fast time series classification under lucky time warping distance

Published: 24 March 2014 Publication History

Abstract

In time series mining, the Dynamic Time Warping (DTW) distance is a commonly and widely used similarity measure. Since the computational complexity of the DTW distance is quadratic, various kinds of warping constraints, lower bounds and abstractions have been developed to speed up time series mining under DTW distance.
In this contribution, we propose a novel Lucky Time Warping (LTW) distance, with linear time and space complexity, which uses a greedy algorithm to accelerate distance calculations for nearest neighbor classification. The results show that, compared to the Euclidean distance (ED) and (un)constrained DTW distance, our LTW distance trades classification accuracy against computational cost reasonably well, and therefore can be used as a fast alternative for nearest neighbor time series classification.

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  • (2024) Efficient Top- k DTW-Based Sensor Data Similarity Search Using Perceptually Important Points and Dual-Bound Filtering IEEE Sensors Journal10.1109/JSEN.2024.347821424:24(41231-41242)Online publication date: 15-Dec-2024
  • (2022)Dynamic Time Warping Under Product Quantization, With Applications to Time-Series Data Similarity SearchIEEE Internet of Things Journal10.1109/JIOT.2021.31320179:14(11814-11826)Online publication date: 15-Jul-2022
  • (2022)Elastic Product Quantization for Time SeriesDiscovery Science10.1007/978-3-031-18840-4_12(157-172)Online publication date: 6-Nov-2022
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cover image ACM Conferences
SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing
March 2014
1890 pages
ISBN:9781450324694
DOI:10.1145/2554850
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 24 March 2014

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Author Tags

  1. classification
  2. distance measures
  3. time series

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SAC 2014
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SAC 2014: Symposium on Applied Computing
March 24 - 28, 2014
Gyeongju, Republic of Korea

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SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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Cited By

View all
  • (2024) Efficient Top- k DTW-Based Sensor Data Similarity Search Using Perceptually Important Points and Dual-Bound Filtering IEEE Sensors Journal10.1109/JSEN.2024.347821424:24(41231-41242)Online publication date: 15-Dec-2024
  • (2022)Dynamic Time Warping Under Product Quantization, With Applications to Time-Series Data Similarity SearchIEEE Internet of Things Journal10.1109/JIOT.2021.31320179:14(11814-11826)Online publication date: 15-Jul-2022
  • (2022)Elastic Product Quantization for Time SeriesDiscovery Science10.1007/978-3-031-18840-4_12(157-172)Online publication date: 6-Nov-2022
  • (2019)A Comprehensive Comparison of Distance Measures for Time Series ClassificationStochastic Models, Statistics and Their Applications10.1007/978-3-030-28665-1_31(409-428)Online publication date: 16-Oct-2019
  • (2018)Scalable Classification of Univariate and Multivariate Time Series2018 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2018.8621889(1598-1605)Online publication date: Dec-2018
  • (2016)Trading off accuracy for efficiency by randomized greedy warpingProceedings of the 31st Annual ACM Symposium on Applied Computing10.1145/2851613.2851651(883-890)Online publication date: 4-Apr-2016
  • (2015)eRingProceedings of the 2nd international Workshop on Sensor-based Activity Recognition and Interaction10.1145/2790044.2790047(1-6)Online publication date: 25-Jun-2015

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