@inproceedings{Souza:2007:cec,
	title = {The Boosting Technique Using Correlation Coefficient
                 to Improve Time Series Forecasting Accuracy},
	address = {Singapore},
	author = {Luzia Vidal {de Souza} and Aurora T. R. Pozo and Joel M. C. {da Rosa} and Anselmo Chaves Neto},
	booktitle = {2007 IEEE Congress on Evolutionary Computation},
	editor = {Dipti Srinivasan and Lipo Wang},
	month = {25-28 September},
	organization = {IEEE Computational Intelligence Society},
	pages = {1288--1295},
	publisher = {IEEE Press},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/23a216ac0670558cbc422b8a9fadc9ba6/brazovayeye},
	abstract = {Time series forecasting has been considered an
                 important tool to support decisions in different
                 domains. A highly accurate prediction is essential to
                 ensure the quality of these decisions. Time series
                 forecasting is based on historical data and the
                 predictions are usually made using statistical methods.
                 These characteristics make the forecasting problem an
                 interesting application of Machine learning techniques,
                 especially for Boosting techniques and Genetic
                 Programming. Boosting techniques 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
                 (GP) and Boosting technique to obtain an ensemble of
                 regressors and proposes a new formula for the updating
                 of the weights and for the final hypothesis. This new
                 formula is based on the correlation coefficient instead
                 of the loss function used by traditional boosting
                 algorithms, this new algorithm is called Boosting using
                 Correlation Coefficient (BCC). To validate this method,
                 experiments were accomplished using real, financial and
                 artificial series generated by Monte Carlo Simulation.
                 The results obtained by using this new methodology were
                 compared with the results obtained from GP, GPBoost and
                 the traditional statistical methodology (ARMA). The
                 results show advantages in the use of the proposed
                 approach.},
	file = {1242.pdf}, isbn = {1-4244-1340-0}, notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and
                 the IET.

                 IEEE Catalog Number: 07TH8963C},
	keywords = {algorithms, genetic programming }
}