Skip to main content
Contribution to Book
AN APPROACH TO FORECASTING BEANPLOT TIME SERIES
Classification and Data Mining. Studies in Classification, Data Analysis, and Knowledge Organization (2013)
  • Carlo Drago
  • Germana Scepi
Abstract

Visualization and Forecasting of time series data is difficult when the data are very numerous, with complex structures as, for example, in the presence of high volatility and structural changes. This is the case of high frequency data or, in general, of financial data, where we cannot clearly visualize the single data and where the necessity of an aggregation arises. In this paper we deal with the specific problem of forecasting beanplot time series. We propose an approach based firstly on a parameterization of the beanplot time series and successively on the chosen best forecasting method with respect to our data. In particular we experiment with a strategy to use combination forecast methods in order to improve the forecasting performance.

Keywords
  • Beanplots,
  • Forecasting,
  • Time Series
Publication Date
Winter January 1, 2013
Editor
Antonio Giusti, Gunter Ritter, Maurizio Vichi
Publisher
Springer Berlin Heidelberg
Citation Information
Carlo Drago and Germana Scepi. "AN APPROACH TO FORECASTING BEANPLOT TIME SERIES" Classification and Data Mining. Studies in Classification, Data Analysis, and Knowledge Organization (2013)
Available at: http://works.bepress.com/carlo_drago/101/