Abstract
Beanplot time series have been introduced by the authors as an aggregated data representation, in terms of peculiar symbolic data, for dealing with large temporal datasets. In the presence of multiple beanplot time series it can be very interesting for interpretative aims to find useful syntheses. Here we propose an extension, based on PCA, of the previous approach to multiple beanplot time series. We show the usefulness of our proposal in the context of the analysis of different financial markets.
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References
A.A.V.V. R Graph Gallery. http://gallery.r-enthusiasts.com/ (2013)
Arroyo, J., Maté, C.: Forecasting histogram time series with k-nearest neighbours methods. International Journal of Forecasting 25(1), 192–207 (2009)
Billard, L., Diday, E.: From the Statistics of Data to the Statistics of Knowledge. Journal of the American Statistical Association 98, 470–487 (2003)
Drago, C., Scepi, G.: Visualizing and exploring high frequency financial data: beanplot time series. In: New Perspectives in Statistical Modeling and Data Analysis: Proceedings of the 7th Conference of the Classification and Data Analysis Group of the Italian Statistical Society, Catania, 2009, vol. 7, p. 283. Springer (9–11 September 2011)
Drago, C., Scepi, G.: Time Series Clustering from High Dimensional Data. Working Paper also presented at CHDD 2012 International Workshop on Clustering High Dimensional Data, Napoli, May 2012
Drago, C., Lauro, C., Scepi, G.: Beanplot Data Analysis in a Temporal Framework Workshop in Symbolic Data Analysis. Working Paper also presented at eight Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, Pavia, September 2011
Du, J.: Combined algorithms for constrained estimation of finite mixture distributions with grouped data and conditional data (Doctoral dissertation, McMaster University) (2002)
Kampstra, P.: Beanplot: A Boxplot Alternative for Visual Comparison of Distributions. Journal Of Statistical Software 28(November), 1–9 (2008)
Macdonald, P. and with contributions from Du, J. mixdist: Finite Mixture Distribution Models. R package (2012)
Sheng, H.Y., Walker, J.J., Ogren, D.E.: A stepwise method for determining the number of component distributions in a mixture. Mathematical Geology 18(2), 153–160 (1986)
Sheather, S.J., Jones, C.: A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society Series B Methodological 53(3), 683–690 (1991)
Verde, R., Irpino, A.: Dynamic clustering of histogram data: using the right metric. In: Brito, P., Bertrand, P., Cucumel, G., De Carvalho, F.: Selected contributions in data analysis and classification, pp. 123–134. Springer, Berlin (2007)
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Drago, C., Lauro, C.N., Scepi, G. (2015). Visualization and Analysis of Multiple Time Series by Beanplot PCA. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_10
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DOI: https://doi.org/10.1007/978-3-319-17091-6_10
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