Abstract
We propose in this work a new approach for modelling, forecasting and clustering beanplot financial time series. The beanplot time series like the histogram time series or the interval time series can be very useful to model the intra-period variability of the series. These types of new time series can be very useful with High Frequency financial data, data collected with often irregularly spaced observations.
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References
Arroyo, J., & Matè, C. (2008). Forecasting histogram time series with the K-neirest neighbour methods. International Journal of Forecasting, 25(2009), 192–207.
Billard, L., & Diday, E. (2006). Symbolic data analysis: conceptual statistics and data mining. Chichester: Wiley.
De Gooijer, J. G., & Hyndman, R. J. (2006). 25 years of time series forecasting. International Journal of Forecasting, 22(3), 443–473. Elsevier
Du, J. (2002). Combined algorithms for constrained estimation of finite mixture distributions with grouped data and conditional data. Department of Mathematics and Statistics, McMaster University.
Gilbert, P. D., & Meijer, E. (2005). Time series factor analysis with an application to measuring money, research report 05F10. University of Groningen, Research Institute SOM (Systems, Organisations and Management).
Jones, M. C., Marron, J. S., & Sheather, S. J. (1996). A brief survey of bandwidth selection for density estimation. Journal of the American Statistical Association, 91, 401–407.
Kampstra, P. (2008). Beanplot: a boxplot alternative for visual comparison of distributions. Journal of Statistical Software, Code Snippets 28(1), 1–9. http://www.jstatsoft.org/v28/c01/.
Romano, E., Giordano, G., & Lauro, C. N. (2006). An inter model distance for clustering utility function, Statistica Applicata, 18(3), 521–533.
Signoriello, S. (2008). Contributions to symbolic data analysis: a model data approach. Ph.D. thesis, Department of Mathematics and Statistics, University of Naples Federico II.
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Drago, C., Lauro, C., Scepi, G. (2013). Beanplot Data Analysis in a Temporal Framework. In: Giudici, P., Ingrassia, S., Vichi, M. (eds) Statistical Models for Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00032-9_14
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DOI: https://doi.org/10.1007/978-3-319-00032-9_14
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