A Generic Preprocessing Optimization Methodology when Predicting Time-Series Data

Ioannis Kyriakidis, Kostas Karatzas, Jonathan Ware, George Papadourakis

Allbwn ymchwil: Cyfraniad at gyfnodolynErthygladolygiad gan gymheiriaid

87 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

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.
Iaith wreiddiolSaesneg
Tudalennau (o-i)638-651
Nifer y tudalennau13
CyfnodolynInternational Journal of Computational Intelligence Systems
Cyfrol9
Rhif cyhoeddi4
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 4 Maw 2016

Ôl bys

Gweld gwybodaeth am bynciau ymchwil 'A Generic Preprocessing Optimization Methodology when Predicting Time-Series Data'. Gyda’i gilydd, maen nhw’n ffurfio ôl bys unigryw.

Dyfynnu hyn