In the parameter tracking of time-varying systems, the ordinary method is weighted least squares with the rectangular window or the exponential window. In this paper we propose a new kind of sliding window called the ...In the parameter tracking of time-varying systems, the ordinary method is weighted least squares with the rectangular window or the exponential window. In this paper we propose a new kind of sliding window called the multiple exponential window, and then use it to fit time-varying Gaussian vector autoregressive models. The asymptotic bias and covariance of the estimator of the parameter for time-invariant models are also derived. Simulation results show that the multiple exponential windows have better parameter tracking effect than rectangular windows and exponential ones.展开更多
This article is devoted to a time series prediction scheme involving the nonlinear autoregressive algorithm and its applications. The scheme is implemented by means of an artificial neural network containing a hidden ...This article is devoted to a time series prediction scheme involving the nonlinear autoregressive algorithm and its applications. The scheme is implemented by means of an artificial neural network containing a hidden layer. As a training algorithm we use scaled conjugate gradient (SCG) method and the Bayesian regularization (BReg) method. The first method is applied to time series without noise, while the second one can also be applied for noisy datasets. We apply the suggested scheme for prediction of time series arising in oil and gas pricing using 50 and 100 past values. Results of numerical simulations are presented and discussed.展开更多
文摘In the parameter tracking of time-varying systems, the ordinary method is weighted least squares with the rectangular window or the exponential window. In this paper we propose a new kind of sliding window called the multiple exponential window, and then use it to fit time-varying Gaussian vector autoregressive models. The asymptotic bias and covariance of the estimator of the parameter for time-invariant models are also derived. Simulation results show that the multiple exponential windows have better parameter tracking effect than rectangular windows and exponential ones.
文摘This article is devoted to a time series prediction scheme involving the nonlinear autoregressive algorithm and its applications. The scheme is implemented by means of an artificial neural network containing a hidden layer. As a training algorithm we use scaled conjugate gradient (SCG) method and the Bayesian regularization (BReg) method. The first method is applied to time series without noise, while the second one can also be applied for noisy datasets. We apply the suggested scheme for prediction of time series arising in oil and gas pricing using 50 and 100 past values. Results of numerical simulations are presented and discussed.
