A three-stage approach for modeling multiple time series applied to symbolic quartile data

Abstract

Data science concerns to look for ways for extracting knowledge from extraordinary volume of data in any domain. This paper introduces a new three-stage approach for time series forecasting based on quartile data that combines aggregation, prototype selection and forecasting in the Symbolic Data Analysis (SDA) framework. Initially, time series are summarized and classes of entities are obtained by representing new time series. A prototype selection process based on mutual information is applied in order to obtain a more representative class data set. Each class is described by a list of three continues values, called quartile symbolic data. Boxplots time series are displayed, as a special case. In this framework, symbolic data are obtained to take into account variability between the entities of the classes. Approaches for boxplots forecasting are constructed from multivariate statistical modeling. Synthetic and real symbolic time series are considered in the experiments to demonstrate the usefulness of the proposed approach.