Title: Classification of time series generation processes using experimental tools: a survey and proposal of an automatic and systematic approach

Authors: Renato P. Ishii; Ricardo A. Rios; Rodrigo F. Mello

Addresses: Department of Computing Systems, Federal University of Mato Grosso do Sul, Campo Grande-MS Caixa Postal 549, 79070-900, Brazil. ' Department of Computer Science, Institute of Mathematics and Computer Sciences, São Carlos-SP 13566-590, Brazil. ' Department of Computer Science, Institute of Mathematics and Computer Sciences, São Carlos-SP 13566-590, Brazil

Abstract: By modelling the outputs produced by real world systems, we can study and, therefore, understand how they work and behave under different circumstances. This is especially interesting to support the prediction of future behaviour and, consequently, decision-making, what is particularly required in certain application domains. In order to proceed with such modelling, we organise system outputs as time series and study how those series were generated. The study of the time series generation process typically requires specialists and also detailed information on how and where data was obtained from. However, none of them may be available in certain circumstances. Such limitations motivated this paper which presents a survey of techniques commonly used to evaluate and classify time series generation processes and, most importantly, a novel automatic and systematic approach to conduct such task with a minimum of human intervention and subjectivity. By using such approach, researchers can select adequate techniques to model time series, reducing the modelling time and improving the chances to obtain higher accuracy.

Keywords: time series generation; modelling; decision making; stochasticity; determinism; linearity; stationarity; experimental tools; time series classification; automatic approach; survey.

DOI: 10.1504/IJCSE.2011.043919

International Journal of Computational Science and Engineering, 2011 Vol.6 No.4, pp.217 - 237

Received: 26 Feb 2011
Accepted: 21 May 2011

Published online: 21 Mar 2015 *

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