A Generic Preprocessing Optimization Methodology when Predicting Time-Series Data

Ioannis Kyriakidis, Kostas Karatzas, Jonathan Ware, George Papadourakis

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Abstract

A general Methodology referred to as Daphne is introduced which is used to find optimum combinations of methods to preprocess and forecast for time-series datasets. The Daphne Optimization Methodology (DOM) is based on the idea of quantifying the effect of each method on the forecasting performance, and using this information as a distance in a directed graph. Two optimization algorithms, Genetic Algorithms and Ant Colony Optimization, were used for the materialization of the DOM. Results show that the DOM finds a near optimal solution in relatively less time than using the traditional optimization algorithms.
Original languageEnglish
Pages (from-to)638-651
Number of pages13
JournalInternational Journal of Computational Intelligence Systems
Volume9
Issue number4
DOIs
Publication statusPublished - 4 Mar 2016

Keywords

  • Preprocessing Optimization Methodology
  • forecasting
  • Genetic Algorithms
  • Ant Colony Optimization

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