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/).
Download article (PDF)
View full text (HTML)
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 -