Nonlinear time series forecasting with Bayesian neural networks

作者：Kocadagli Ozan^*; Asikgil Baris

来源：Expert Systems with Applications, 2014, 41(15): 6596-6610.

DOI：10.1016/j.eswa.2014.04.035

• Nonlinear time series
• Bayesian neural networks
• Gaussian approximation
• Recursive hyperparameters
• Genetic algorithms
• Hybrid Monte Carlo simulations

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.

• 出版日期2014-11-1