Classification of time series by shapelet transformation
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2013/05/18
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Indian UGC (journal)
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Refrences42
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Citations222
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Jon Hills
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Jason Lines
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Edgaras Baranauskas
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James Mapp
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Anthony Bagnall
Cite
Hills, Jon, et al. “Classification of Time Series by Shapelet Transformation”. Data Mining and Knowledge Discovery, vol. 28, no. 4, 2013, pp. 851-8, https://doi.org/10.1007/s10618-013-0322-1.
Hills, J., Lines, J., Baranauskas, E., Mapp, J., & Bagnall, A. (2013). Classification of time series by shapelet transformation. Data Mining and Knowledge Discovery, 28(4), 851-881. https://doi.org/10.1007/s10618-013-0322-1
Hills, Jon, Jason Lines, Edgaras Baranauskas, James Mapp, and Anthony Bagnall. “Classification of Time Series by Shapelet Transformation”. Data Mining and Knowledge Discovery 28, no. 4 (2013): 851-81. https://doi.org/10.1007/s10618-013-0322-1.
Hills J, Lines J, Baranauskas E, Mapp J, Bagnall A. Classification of time series by shapelet transformation. Data Mining and Knowledge Discovery. 2013;28(4):851-8.
Journal Categories
Science
Mathematics
Instruments and machines
Electronic computers
Computer science
Science
Science (General)
Cybernetics
Information theory
Technology
Electrical engineering
Electronics
Nuclear engineering
Electronics
Technology
Mechanical engineering and machinery
Refrences
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
| Early classification on time series | Knowledge and Information Systems |
| 65 | 2012 |
| Time series shapelets: a novel technique that allows accurate, interpretable and fast classification | Data Mining and Knowledge Discovery |
| 146 | 2011 |
The WEKA data mining software | ACM SIGKDD Explorations Newsletter | 6,582 | 2009 | |
The use of sagittal otoliths in discriminating stocks of common dolphinfish (Coryphaena hippurus) off northeastern Brazil using multishape descriptors | ICES Journal of Marine Science |
| 40 | 2008 |
Querying and mining of time series data | Proceedings of the VLDB Endowment |
| 450 | 2008 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Bake off redux: a review and experimental evaluation of recent time series classification algorithms | Data Mining and Knowledge Discovery |
| 2024 | |
Finding Discriminative Subsequences Via a Coverage Measure and Mutual Information Selection Strategy for Multi-Class Time Series Classification | International Journal of Computational Intelligence Systems |
| 2024 | |
| Scalable and accurate subsequence transform for time series classification | Pattern Recognition |
| 2024 | |
Table tennis motion recognition based on the bat trajectory using varying-length-input convolution neural networks | Scientific Reports |
| 2024 | |
| Domain Adaptation of Time Series Classification | IEEE Access |
| 2024 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 124
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled A DISCRIMINATIVE SHAPELETS TRANSFORMATION FOR TIME SERIES CLASSIFICATION and was published in 2014. The most recent citation comes from a 2024 study titled Wide-TSNet: A Novel Hybrid Approach for Bitcoin Price Movement Classification. This article reached its peak citation in 2022, with 41 citations. It has been cited in 106 different journals, 16% of which are open access. Among related journals, the Data Mining and Knowledge Discovery cited this research the most, with 19 citations. The chart below illustrates the annual citation trends for this article.