Abstract
The analysis of time series databases is very important in areas like medicine, engineering or finance. Most of the approaches that address this problem are based on numerical algorithms calculating distances, clusters, index trees, etc. We have developed a numerical pattern discovery algorithm to find similar patterns to characterize time series, with good results in the isokinetics domain.
However, it is sometimes necessary to conduct a domain-dependent analysis, searching for symbolic rather than numerical characteristics of the time series. For this purpose, we have designed a symbol extraction method that translates a numerical sequence into a symbolic one with a semantic value in a particular domain. This method provides a semi-analyzed symbolic series, in which the main characteristics of the numerical series have been discovered. So, this symbolic series help users to efficiently analyze time series similarly to how an expert in the domain would do.
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Santamaría, A., López-Illescas, Á., Perez-Perez, A., Caraça-Valente, J.P. (2005). Towards Symbolic Data Mining in Numerical Time Series. In: Bramer, M., Coenen, F., Allen, T. (eds) Research and Development in Intelligent Systems XXI. SGAI 2004. Springer, London. https://doi.org/10.1007/1-84628-102-4_17
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DOI: https://doi.org/10.1007/1-84628-102-4_17
Publisher Name: Springer, London
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