An empirical study of time series forecasting using boosting technique with correlation coefficient

Luzia Vidal De Souza, Aurora T.R. Pozo, Anselmo Chaves Neto, Joel M.Corrêa Da Rosa

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

One of the most important fields of researches and applications is time series forecasting. The task to find a model that can fit the data is not easy, because the most of the problems the series are complex and noisy. Recently, ensemble of machines had been used to get accurate predictions. The mean idea is to combine predictions from different forecast methods in only one predictor and in this way to improve the accuracy. This paper explores Genetic Programming (GP) and Boosting technique to obtain an ensemble of predictors and proposes a new approach to the Boosting algorithm where the correlation coefficients are used to update the weights and the final hypothesis instead of the loss function used traditionally by the boosting algorithm. To validate this method, experiments were accomplished using real and artificial series generated by Monte Carlo Simulation. The results obtained by using this new methodology was compared with the results obtained from GP, GPBoost and the traditional statistical methodology (ARMA).

Original languageEnglish
Title of host publicationProceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007
Pages807-812
Number of pages6
DOIs
StatePublished - 2007
Externally publishedYes
Event7th International Conference on Intelligent Systems Design and Applications, ISDA'07 - Rio de Janeiro, Brazil
Duration: 22 Oct 200724 Oct 2007

Publication series

NameProceedings of The 7th International Conference on Intelligent Systems Design and Applications, ISDA 2007

Conference

Conference7th International Conference on Intelligent Systems Design and Applications, ISDA'07
Country/TerritoryBrazil
CityRio de Janeiro
Period22/10/0724/10/07

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