期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 41, 期 15, 页码 6596-6610出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2014.04.035
关键词
Nonlinear time series; Bayesian neural networks; Gaussian approximation; Recursive hyperparameters; Genetic algorithms; Hybrid Monte Carlo simulations
类别
资金
- Scientific and Technological Research Council of Turkey (TUBITAK)
The Bayesian learning provides a natural way to model the nonlinear structure as the artificial neural networks due to their capability to cope with the model complexity. In this paper, an evolutionary Monte Carlo (MC) algorithm is proposed to train the Bayesian neural networks (BNNs) for the time series forecasting. This approach called as Genetic MC is based on Gaussian approximation with recursive hyperparameter. Generic MC integrates MC simulations with the genetic algorithms and the fuzzy membership functions. In the implementations, Genetic MC is compared with the traditional neural networks and time series techniques in terms of their forecasting performances over the weekly sales of a Finance Magazine. (C) 2014 Elsevier Ltd. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据