A Generic Preprocessing Optimization Methodology when Predicting Time-Series Data. Issue 4 (3rd July 2016)

• Record Type: Journal Article
• Authors: Kyriakidis, Ioannis; Karatzas, Kostas; Ware, Andrew; Papadourakis, George
• Is Part Of: International journal of computational intelligence systems. Volume 9:Issue 4(2016)
• Page Start: 638
• Page End: 651
• Publication Date: 2016-07-03
• Subjects: Preprocessing Optimization Methodology -- forecasting -- Genetic Algorithms -- Ant Colony Optimization; Computational intelligence -- Periodicals; 006.305
• DOI: 10.1080/18756891.2016.1204113
• Languages: English
• ISSNs: 1875-6891

Abstract

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.