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Comparing three lower bounding methods for DTW in time series classification

Published: 23 August 2012 Publication History

Abstract

In comparison to Euclidean distance, Dynamic Time Warping (DTW) is a much more robust distance measure for time series data. For speeding up DTW computation, a few lower bounding techniques have been proposed in literature to guarantee no false dismissals in time series similarity search. In this work, we apply DTW lower bounding method in time series classification and empirically compare three different typical lower bounding techniques for DTW: LB_Keogh, FTW and LB_Improved in this time series data mining task. Our experimental results show that LB_Keogh and LB_Improved perform well with small warping window widths while FTW is only suitable with large warping window widths or without any constraint on warping windows. Besides, runtime efficiency of LB_Improved is quite poor due to its high complexity in lower bound computation despite of its better pruning power.

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Nguyen Cong Thuong. 2009. Anytime Classification Algorithm for Time Series Data, Master Thesis, Faculty of Computer Science and Engineering, Ho Chi Minh City University, Vietnam.
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Cited By

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  • (2023)Urban Mobility and Knowledge Extraction from Chaotic Time Series Data: A Comparative Analysis for Uncovering COVID-19 EffectsAnnals of the American Association of Geographers10.1080/24694452.2023.2216773113:9(2166-2185)Online publication date: 20-Jul-2023
  • (2014)Adding Diversity to Rank Examples in Anytime Nearest Neighbor ClassificationProceedings of the 2014 13th International Conference on Machine Learning and Applications10.1109/ICMLA.2014.26(129-134)Online publication date: 3-Dec-2014

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cover image ACM Other conferences
SoICT '12: Proceedings of the 3rd Symposium on Information and Communication Technology
August 2012
290 pages
ISBN:9781450312325
DOI:10.1145/2350716
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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 August 2012

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Author Tags

  1. anytime algorithm
  2. dynamic time warping
  3. lower bounding techniques
  4. time series
  5. time series classification

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View all
  • (2023)Urban Mobility and Knowledge Extraction from Chaotic Time Series Data: A Comparative Analysis for Uncovering COVID-19 EffectsAnnals of the American Association of Geographers10.1080/24694452.2023.2216773113:9(2166-2185)Online publication date: 20-Jul-2023
  • (2014)Adding Diversity to Rank Examples in Anytime Nearest Neighbor ClassificationProceedings of the 2014 13th International Conference on Machine Learning and Applications10.1109/ICMLA.2014.26(129-134)Online publication date: 3-Dec-2014

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