DocumentCode :
2922466
Title :
Using Correlation to Improve Boosting Technique: An Application for Time Series Forecasting
Author :
De Souza, Luzia Vidal ; Pozo, Aurora T Ramirez ; Neto, Anselmo Chaves
Author_Institution :
Dept. of Design, Fed. Univ. of Parana, Curitiba
fYear :
2006
fDate :
Nov. 2006
Firstpage :
26
Lastpage :
32
Abstract :
Time series forecasting has been widely used to support decision making, in this context a highly accurate prediction is essential to ensure the quality of the decisions. Ensembles of machines currently receive a lot of attention; they combine predictions from different forecasting methods as a procedure to improve the accuracy. This paper explores genetic programming and boosting technique to obtain an ensemble of regressors and proposes a new formula for the final hypothesis. This new formula is based on the correlation coefficient instead of the geometric median used by the boosting algorithm. To validate this method, experiments were performed, the mean squared error (MSE) has been used to compare the accuracy of the proposed method against the results obtained by GP, GP using a boosting technique and the traditional statistical methodology (ARMA). The results show advantages in the use of the proposed approach
Keywords :
autoregressive moving average processes; decision making; forecasting theory; genetic algorithms; learning (artificial intelligence); mean square error methods; time series; ARMA; boosting; correlation coefficient; decision making; genetic programming; mean squared error; statistical methodology; time series forecasting; Artificial neural networks; Boosting; Classification algorithms; Economic forecasting; Evolutionary computation; Genetic programming; Machine learning; Machine learning algorithms; Predictive models; Stock markets;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Tools with Artificial Intelligence, 2006. ICTAI '06. 18th IEEE International Conference on
Conference_Location :
Arlington, VA
ISSN :
1082-3409
Print_ISBN :
0-7695-2728-0
Type :
conf
DOI :
10.1109/ICTAI.2006.118
Filename :
4031876
Link To Document :
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