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Computer Science and Information Engineering, World Congress on (2009)
Los Angeles, California USA
Mar. 31, 2009 to Apr. 2, 2009
ISBN: 978-0-7695-3507-4
pp: 490-494
Time series data generation has exploded in almost every domain such as in business, industry, or medicine. The demand for analyzing efficiently the huge amount of this information necessitates the application of a representation on the purpose of reducing the intrinsically high dimensionality of time series. In this paper we introduce DPAA, a new representation that can be considered as a variation of Piecewise Aggregate Approximation (PAA). DPAA segments a time series into a series of equal length sections and the corresponding mean and standard deviation are recorded for each one of them. The difference with PAA is that DPAA takes into consideration not only the central tendency but also the dispersion present in each section. We evaluate our representation by applying 1-NN classification on 20 widely utilized datasets in the literature. Experimental results indicate that the proposed representation performs better than other commonly applied representations in the majority of the datasets.
time series data mining, dimensionality reduction, time series representations

G. Evangelidis and L. Karamitopoulos, "A Dispersion-Based PAA Representation for Time Series," 2009 WRI World Congress on Computer Science and Information Engineering, CSIE(CSIE), Los Angeles, CA, 2009, pp. 490-494.
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