TY  - CONF
TI  - Variance-covariance based weighing for neural network ensembles
ID  - scholars3294
SP  - 3214
KW  - Artificial intelligence techniques; Forecast combinations; Forecasting accuracy; Load demand; Neural network (nn); Neural network ensembles; Variance-covariance; Weighted averaging
KW  -  Cybernetics; Electric network topology; Experiments; Neural networks
KW  -  Forecasting
N1  - cited By 1; Conference of 2013 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2013 ; Conference Date: 13 October 2013 Through 16 October 2013; Conference Code:102429
N2  - Neural network (NN) is a popular artificial intelligence technique for solving complicated problems due to their inherent capabilities. However generalization in NN can be harmed by a number of factors including parameter's initialization, inappropriate network topology and setting parameters of the training process itself. Forecast combinations of NN models have the potential for improved generalization and lower training time. A weighted averaging based on Variance-Covariance method that assigns greater weight to the forecasts producing lower error, instead of equal weights is practiced in this paper. While implementing the method, combination of forecasts is done with all candidate models in one experiment and with the best selected models in another experiment. It is observed during the empirical analysis that forecasting accuracy is improved by combining the best individual NN models. Another finding of this study is that reducing the number of NN models increases the diversity and, hence, accuracy. © 2013 IEEE.
AV  - none
CY  - Manchester
EP  - 3219
A1  - Hassan, S.
A1  - Khosravi, A.
A1  - Jaafar, J.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893616999&doi=10.1109%2fSMC.2013.548&partnerID=40&md5=aa07a4a882d3d2060e6e4f3b254b3dbb
SN  - 9780769551548
Y1  - 2013///
ER  -