GEM: an elastic and translation-invariant similarity measure with automatic trend adjustment
Authors: 
Christian Hundt, Bertil Schmidt, Elmar Schömer, Herbert Göttler, Hoang-Vu DangAuthors Info & Claims
Christian Hundt, Bertil Schmidt, Elmar Schömer, Herbert Göttler, Hoang-Vu DangAuthors Info & ClaimsPages 105 - 112
Abstract
The widespread use of digital sensor systems causes a tremendous demand for high-quality time series analysis tools. In this domain the majority of data mining algorithms relies on established distance measures like Dynamic Time Warping (DTW) or Euclidean distance (ED). However, the notion of similarity induced by ED and DTW may lead to unsatisfactory clusterings. In order to address this shortcoming we introduce the Gliding Elastic Match (GEM) algorithm. It determines an optimal local similarity measure of a query time series Q and a subject time series S. The measure is invariant under both local deformation on the measurement-axis and scaling in the time domain. GEM is compared to ED and (un)constrained DTW in terms of matching quality for several datasets and outperforms the competitors in most cases. In order to accelerate the application of GEM to large-scale datasets, we further present an efficient CUDA parallelization.
References
[1]
D. Lee, J. K. Archibald, R. B. Schoenberger, A. W. Dennis, and D. K. Shiozawa, "Contour Matching for Fish Species Recognition and Migration Monitoring," in Applications of Computational Intelligence in Biology, ser. Studies in Computational Intelligence. Springer Berlin Heidelberg, 2008, vol. 122, pp. 183--207.
[2]
C. A. Ratanamahatana and E. Keogh, "Making Time-series Classification More Accurate Using Learned Constraints," in In proc. of SDM IntâĂŹl Conf, 2004, pp. 11--22.
[3]
G. Batista, X. Wang, and E. Keogh, "A complexity-invariant distance measure for time series." in SDM. SIAM / Omnipress, 2011, pp. 699--710.
[4]
E. Keogh, Q. Zhu, B. Hu, Y. Hao., X. Xi, L. Wei, and C. A. Ratanamahatana, "The UCR Time Series Classification/Clustering Homepage," 2011.
[5]
J. Zakaria, A. Mueen, and E. Keogh, "Clustering Time Series Using Unsupervised-Shapelets." in ICDM. IEEE Computer Society, 2012, pp. 785--794.
[6]
"supplementary webpage." {Online}. Available: https://gravitino.github.com/gem
[7]
D. J. Berndt and J. Clifford, "Using Dynamic Time Warping to Find Patterns in Time Series," in KDD Workshop, 1994, pp. 359--370.
[8]
V. Levenshtein, "Binary Codes Capable of Correcting Deletions, Insertions, and Reversals," Soviet Physics Doklady, vol. 10, no. 8, pp. 707--710, 1966.
[9]
L. Chen, M. T. Özsu, and V. Oria, "Robust and Fast Similarity Search for Moving Object Trajectories," in Proceedings of the 2005 ACM SIGMOD international conference on Management of data, ser. SIGMOD '05. New York, NY, USA: ACM, 2005, pp. 491--502.
[10]
L. Chen and R. Ng, "On the Marriage of Lp-norms and Edit Distance," in Proceedings of the Thirtieth international conference on Very large data bases - Volume 30, ser. VLDB '04. VLDB Endowment, 2004, pp. 792--803.
[11]
M. Vlachos, M. Hadjieleftheriou, D. Gunopulos, and E. Keogh, "Indexing Multi-Dimensional Time-Series with Support for Multiple Distance Measures," in Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining, ser. KDD '03. New York, NY, USA: ACM, 2003, pp. 216--225.
[12]
L. J. Latecki, V. Megalooikonomou, Q. Wang, R. Lakaemper, C. A. Ratanamahatana, and E. Keogh, "Partial Elastic Matching of Time Series," in Proceedings of the Fifth IEEE International Conference on Data Mining, ser. ICDM '05. Washington, DC, USA: IEEE Computer Society, 2005, pp. 701--704.
[13]
T. F. Smith and M. S. Waterman, "Identification of Common Molecular Subsequences." Journal of molecular biology, vol. 147, no. 1, pp. 195--197, Mar. 1981.
[14]
Y. Sakurai, C. Faloutsos, and M. Yamamuro, "Stream monitoring under the time warping distance," in Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on, 2007, pp. 1046--1055.
[15]
D. Sart, A. Mueen, W. Najjar, E. Keogh, and V. Niennattrakul, "Accelerating Dynamic Time Warping Subsequence Search with GPUs and FPGAs," in Proceedings of the 2010 IEEE International Conference on Data Mining, ser. ICDM '10. Washington, DC, USA: IEEE Computer Society, 2010, pp. 1001--1006.
[16]
T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, and E. Keogh, "Searching and mining trillions of time series subsequences under dynamic time warping," in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining, ser. KDD '12. New York, NY, USA: ACM, 2012, pp. 262--270.
[17]
F. Klinker, "Exponential moving average versus moving exponential average," Mathematische Semesterberichte, vol. 58, pp. 97--107, 2011.
[18]
C. Nadeau and Y. Bengio, "Inference for the Generalization Error," Mach. Learn., vol. 52, no. 3, pp. 239--281, Sep. 2003.
Index Terms
- GEM: an elastic and translation-invariant similarity measure with automatic trend adjustment
Recommendations
cuDTW++: Ultra-Fast Dynamic Time Warping on CUDA-Enabled GPUs
Euro-Par 2020: Parallel ProcessingAbstractDynamic Time Warping (DTW) is a widely used distance measure in the field of time series data mining. However, calculation of DTW scores is compute-intensive since the complexity is quadratic in terms of time series lengths. This renders important ...
A new similarity measure between intuitionistic fuzzy sets and the positive definiteness of the similarity matrix
As a generation of fuzzy set theory, intuitionistic fuzzy (IF) set theory has received considerable attention for its capability on dealing with uncertainty. Similarity measures of IF sets are used to indicate the degree of commonality between IF sets. ...
Clustering of interval-valued time series of unequal length based on improved dynamic time warping
AbstractClustering of a group of interval-valued time series of unequal length is often encountered and the key point of this clustering is the distance measure between two interval-valued time series. However, most distance measure methods ...
Comments
Information & Contributors
Information
Published In
March 2014
1890 pages
ISBN:9781450324694
DOI:10.1145/2554850
- Conference Chairs:
- Yookun Cho,
- Sung Y. Shin,
- Program Chairs:
- Sangwook Kim,
- Chih-Cheng Hung,
- Jiman Hong
Copyright © 2014 ACM.
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]
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 24 March 2014
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
SAC 2014
Sponsor:
Acceptance Rates
SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;
Overall Acceptance Rate 1,650 of 6,669 submissions, 25%
Upcoming Conference
SAC '25
- Sponsor:
- sigapp
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 97Total Downloads
- Downloads (Last 12 months)1
- Downloads (Last 6 weeks)0
Reflects downloads up to 27 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in