International Journal of Computational Intelligence Systems

Volume 9, Issue 4, August 2016, Pages 638 - 651

A Generic Preprocessing Optimization Methodology when Predicting Time-Series Data

Authors
Ioannis Kyriakidiskyriakidis@teicrete.gr
School of Computing and Mathematics, University of South Wales, Treforest, Pontypridd, CF37 1DL, United Kingdom
Kostas Karatzaskkara@eng.auth.gr
Department of Mechanical Engineering, Aristotle University, Thessaloniki, GR-54124, Greece
Andrew Wareandrew.ware@southwales.ac.uk
School of Computing and Mathematics, University of South Wales, Treforest, Pontypridd, CF37 1DL, United Kingdom
George Papadourakispapadour@cs.teicrete.gr
Department of Informatics Engineering, T.E.I. of Crete, Estavromenos, GR-71004 Heraklion, Crete, Greece
Received 13 July 2015, Accepted 4 March 2016, Available Online 1 August 2016.
DOI
10.1080/18756891.2016.1204113How to use a DOI?
Keywords
Preprocessing Optimization Methodology; forecasting; Genetic Algorithms; Ant Colony Optimization
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.

Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
9 - 4
Pages
638 - 651
Publication Date
2016/08/01
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.1080/18756891.2016.1204113How to use a DOI?
Copyright
© 2016. the authors. Co-published by Atlantis Press and Taylor & Francis
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Ioannis Kyriakidis
AU  - Kostas Karatzas
AU  - Andrew Ware
AU  - George Papadourakis
PY  - 2016
DA  - 2016/08/01
TI  - A Generic Preprocessing Optimization Methodology when Predicting Time-Series Data
JO  - International Journal of Computational Intelligence Systems
SP  - 638
EP  - 651
VL  - 9
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.1080/18756891.2016.1204113
DO  - 10.1080/18756891.2016.1204113
ID  - Kyriakidis2016
ER  -