In this paper we deal with the problem of visualizing and exploring specific time series such as high-frequency financial data. These data present unique features, absent in classical time series, which involve the necessity of searching and analysing an aggregate behaviour. Therefore, we define peculiar aggregated time series called beanplot time series. We show the advantages of using them instead of scalar time series when the data have a complex structure. Furthermore, we underline the interpretative proprieties of beanplot time series by comparing different types of aggregated time series. In particular, with simulated and real examples, we illustrate the different statistical performances of beanplot time series respect to boxplot time series.
- High Frequency Data
Available at: http://works.bepress.com/carlo_drago/96/