Advances in computer technology have made large data ubiquitous and have determined the need to handles these data accordingly. In particular these data need to be aggregated using some function, but this process can lead to a loss of information. Beanplot series in this context can represent a solution in terms of special symbolic data: in fact the parameters of the density models based on a mixture of distribution can represent the original data accordingly and contribute to solve the problem of data storage. In this work we propose an approach to beanplot data analysis by PCA on the parameters of the models. The aim is building synthesis of multiple beanplot time series as indicators which can have relevant applications in Finance, in Risk Management and in other disciplines.
- Time Series,
- Symbolic Data Analysis,
- Principal Component Analysis
Available at: http://works.bepress.com/carlo_drago/120/