Experiencing SAX: a novel symbolic representation of time series
Article Properties
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LanguageEnglish
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DOI (url)
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Publication Date2007/04/03
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Indian UGC (journal)
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Refrences63
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Citations606
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Jessica Lin
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Eamonn Keogh
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Li Wei
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Stefano Lonardi
Cite
Lin, Jessica, et al. “Experiencing SAX: A Novel Symbolic Representation of Time Series”. Data Mining and Knowledge Discovery, vol. 15, no. 2, 2007, pp. 107-44, https://doi.org/10.1007/s10618-007-0064-z.
Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. https://doi.org/10.1007/s10618-007-0064-z
Lin, Jessica, Eamonn Keogh, Li Wei, and Stefano Lonardi. “Experiencing SAX: A Novel Symbolic Representation of Time Series”. Data Mining and Knowledge Discovery 15, no. 2 (2007): 107-44. https://doi.org/10.1007/s10618-007-0064-z.
1.
Lin J, Keogh E, Wei L, Lonardi S. Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery. 2007;15(2):107-44.
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 |
|---|---|---|---|---|
Visualizing and Discovering Non-Trivial Patterns in Large Time Series Databases | Information Visualization |
| 41 | 2005 |
| 10.1007/PL00011669 | 2001b | |||
| Probabilistic and Statistical Properties of Words: An Overview | Journal of Computational Biology |
| 114 | 2000 |
| Daw CS, Finney CEA, Tracy ER (2001) Symbolic analysis of experimental data. Review of Scientific Instruments 74:915–930 | 2001 | |||
| Staden R (1989) Methods for discovering novel motifs in nucleic acid sequences. Comput Appl Biosci 5:293–298 | 1989 |
Citations
| Title | Journal | Journal Categories | Citations | Publication Date |
|---|---|---|---|---|
Handling of problematic ion chromatograms with the Automated Target Screening (ATS) workflow for unsupervised analysis of high-resolution mass spectrometry data | Analytical and Bioanalytical Chemistry |
| 2024 | |
| Comparing dimensionality reduction techniques for visual analysis of the LSTM hidden activity on multi-dimensional time series modeling | The Visual Computer |
| 2024 | |
Bake off redux: a review and experimental evaluation of recent time series classification algorithms | Data Mining and Knowledge Discovery |
| 2024 | |
| Fast and accurate ECG signal peaks detection using symbolic aggregate approximation | Multimedia Tools and Applications |
| 1 | 2024 |
An Application of Fuzzy Symbolic Time-Series for Energy Demand Forecasting | International Journal of Fuzzy Systems |
| 2024 |
Citations Analysis
The category
Science: Mathematics: Instruments and machines: Electronic computers. Computer science 305
is the most commonly referenced area in studies that cite this article. The first research to cite this article was titled Knowledge construction from time series data using a collaborative exploration system and was published in 2007. The most recent citation comes from a 2024 study titled SEGAL time series classification — Stable explanations using a generative model and an adaptive weighting method for LIME. This article reached its peak citation in 2020, with 72 citations. It has been cited in 296 different journals, 19% of which are open access. Among related journals, the Data Mining and Knowledge Discovery cited this research the most, with 29 citations. The chart below illustrates the annual citation trends for this article.