Abstract
The comparison of two time series and the extraction of subsequences that are common to the two is a complex data mining problem. Many existing techniques, like the Discrete Fourier Transform (DFT), offer solutions for comparing two whole time series. Often, however, the important thing is to analyse certain regions, known as events, rather than the whole times series. This applies to domains like the stock market, seismography or medicine. In this paper, we propose a method for comparing two time series by analysing the events present in the two. The proposed method is applied to time series generated by stabilometric and posturographic systems within a branch of medicine studying balance-related functions in human beings.
This work was funded by the Spanish Ministry of Education and Science as part of the 2004-2007 National R&D&I Plan through the VIIP Project (DEP2005-00232-C03).
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Lara, J.A., Pérez, A., Valente, J.P., López-Illescas, Á. (2009). Comparing Time Series through Event Clustering. In: Corchado, J.M., De Paz, J.F., Rocha, M.P., Fernández Riverola, F. (eds) 2nd International Workshop on Practical Applications of Computational Biology and Bioinformatics (IWPACBB 2008). Advances in Soft Computing, vol 49. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85861-4_1
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DOI: https://doi.org/10.1007/978-3-540-85861-4_1
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