Time series representation and similarity based on local autopatterns
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2015/07/07
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Indian UGC (journal)
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Refrences55
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Citations82
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Mustafa Gokce Baydogan
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George Runger
Cite
Baydogan, Mustafa Gokce, and George Runger. “Time Series Representation and Similarity Based on Local Autopatterns”. Data Mining and Knowledge Discovery, vol. 30, no. 2, 2015, pp. 476-09, https://doi.org/10.1007/s10618-015-0425-y.
Baydogan, M. G., & Runger, G. (2015). Time series representation and similarity based on local autopatterns. Data Mining and Knowledge Discovery, 30(2), 476-509. https://doi.org/10.1007/s10618-015-0425-y
Baydogan, Mustafa Gokce, and George Runger. “Time Series Representation and Similarity Based on Local Autopatterns”. Data Mining and Knowledge Discovery 30, no. 2 (2015): 476-509. https://doi.org/10.1007/s10618-015-0425-y.
1.
Baydogan MG, Runger G. Time series representation and similarity based on local autopatterns. Data Mining and Knowledge Discovery. 2015;30(2):476-509.
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 |
|---|---|---|---|---|
| Classification of time series by shapelet transformation | Data Mining and Knowledge Discovery |
| 222 | 2014 |
| Invariant time-series factorization | Data Mining and Knowledge Discovery |
| 8 | 2014 |
| CID: an efficient complexity-invariant distance for time series | Data Mining and Knowledge Discovery |
| 165 | 2014 |
| 10.1109/TPAMI.2013.72 | IEEE Transactions on Pattern Analysis and Machine Intelligence |
| 2013 | |
| Experimental comparison of representation methods and distance measures for time series data | Data Mining and Knowledge Discovery |
| 369 | 2013 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Universal representation learning for multivariate time series using the instance-level and cluster-level supervised contrastive learning | Data Mining and Knowledge Discovery |
| 2024 | |
Bake off redux: a review and experimental evaluation of recent time series classification algorithms | Data Mining and Knowledge Discovery |
| 2024 | |
TSCF: An Improved Deep Forest Model for Time Series Classification | Neural Processing Letters |
| 2024 | |
| MultiBEATS: Blocks of eigenvalues algorithm for multivariate time series dimensionality reduction | Information Fusion |
| 1 | 2024 |
| Deep Contrastive Representation Learning With Self-Distillation | IEEE Transactions on Emerging Topics in Computational Intelligence |
| 2 | 2024 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 63
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances and was published in 2016. The most recent citation comes from a 2024 study titled Bake off redux: a review and experimental evaluation of recent time series classification algorithms. This article reached its peak citation in 2021, with 22 citations. It has been cited in 44 different journals, 15% of which are open access. Among related journals, the Data Mining and Knowledge Discovery cited this research the most, with 8 citations. The chart below illustrates the annual citation trends for this article.