CID: an efficient complexity-invariant distance for time series
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2013/04/12
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Indian UGC (journal)
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Refrences38
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Citations165
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Gustavo E. A. P. A. Batista
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Eamonn J. Keogh
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Oben Moses Tataw
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Vinícius M. A. de Souza
Cite
Batista, Gustavo E. A. P. A., et al. “CID: An Efficient Complexity-Invariant Distance for Time Series”. Data Mining and Knowledge Discovery, vol. 28, no. 3, 2013, pp. 634-69, https://doi.org/10.1007/s10618-013-0312-3.
Batista, G. E. A. P. A., Keogh, E. J., Tataw, O. M., & de Souza, V. M. A. (2013). CID: an efficient complexity-invariant distance for time series. Data Mining and Knowledge Discovery, 28(3), 634-669. https://doi.org/10.1007/s10618-013-0312-3
Batista, Gustavo E. A. P. A., Eamonn J. Keogh, Oben Moses Tataw, and Vinícius M. A. de Souza. “CID: An Efficient Complexity-Invariant Distance for Time Series”. Data Mining and Knowledge Discovery 28, no. 3 (2013): 634-69. https://doi.org/10.1007/s10618-013-0312-3.
Batista GEAPA, Keogh EJ, Tataw OM, de Souza VMA. CID: an efficient complexity-invariant distance for time series. Data Mining and Knowledge Discovery. 2013;28(3):634-69.
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 |
|---|---|---|---|---|
| Finding outlier light curves in catalogues of periodic variable stars | Monthly Notices of the Royal Astronomical Society |
| 38 | 2006 |
| 10.1161/01.CIR.101.23.e215 | Circulation |
| 2000 | |
| 10.1002/1097-0193(200009)11:1<46::AID-HBM40>3.0.CO;2-5 | Human Brain Mapping |
| 2000 | |
| Stochastic complexity measures for physiological signal analysis | IEEE Transactions on Biomedical Engineering |
| 146 | 1998 |
| Objective Criteria for the Evaluation of Clustering Methods | Journal of the American Statistical Association |
| 2,337 | 1971 |
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 | |
| A query-response causal analysis of reaction events in biochemical reaction networks | Computational Biology and Chemistry |
| 2024 | |
| A new similarity measurement method for time series based on image fusion of recurrence plots and wavelet scalogram | Engineering Applications of Artificial Intelligence |
| 1 | 2024 |
| A novel clustering method for complex signals and feature extraction based on advanced information-based dissimilarity measure | Expert Systems with Applications |
| 2024 | |
| Detection of abnormal operation in geothermal binary plant feed pumps using time-series analytics | Expert Systems with Applications |
| 2024 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 73
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Invariant time-series factorization and was published in 2014. 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 2020, with 26 citations. It has been cited in 101 different journals, 13% of which are open access. Among related journals, the Data Mining and Knowledge Discovery cited this research the most, with 7 citations. The chart below illustrates the annual citation trends for this article.