Using Correlation to Improve Boosting Technique: An Application for Time Series Forecasting
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.
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
Correlation coefficient,Time series,Pattern recognition,Computer science,Mean squared error,Genetic programming,Artificial intelligence,Boosting (machine learning),Geometric median,Genetic algorithm,Machine learning,Gradient boosting