An Approach to Forecasting Beanplot Time Series

Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

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

Forecast Model Smoothing Spline Vector Error Correction Model High Frequency Data Attribute Time Series 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.University of Naples Federico IIMonte Sant’Angelo (NA)Italy

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