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We illustrate the AutoRegessive/Generalised Conditionally Heterosscedastic (ARCH-GARCH) methodology on the developing a forecast model for exchange rates time series of the Czech crown (CZK) against the Slovak crown (SKK) and make comparisons the forecast accuracy with the class of Radial Basic Function Neural neural network RBF NN models. To illustrate the forecasting performance of these approaches...
Automobile sells system plays an important role in automobile sales area, through the whole produce and management. Some forecast models have had unilateralism in some side nowadays, such as ARMA model. For example, the data of non-linearity has some error by ARMA model. This paper, assembles curve -regression model, Time Series Decomposition Model and RBF neural networks according to the weight distribution...
Economic growth forecasting is important to make the policy on national economic development. Support vector machine (SVM) is a new machine learning method, which seeks to minimize an upper bound of the generalization error instead of the empirical error as in conventional neural networks. In the study, support vector machine and particle swarm optimization is applied in economic growth forecasting,...
Radial basis function neural networks have been successfully applied to time series prediction in literature. Frequently, methods to build and train these networks must be given the past periods or lags to be used in order to create patterns and forecast any time series. This paper introduces E-tsRBF, a meta-evolutionary algorithm that evolves both the neural networks and the set of lags needed to...
The CNY exchange rates can be viewed as financial time series which are charactered by high uncertainty, nonlinearity and time-varying behavior. Predictions for exchange rates of GBP-CNY and USD-CNY were carried respectively by means of RBF neural network forecasters. The detailed designs for architectures of RBF neural network models, transfer functions of the hidden layer nodes, input vectors and...
In construction cost forecasting system, a great many uncertain factors effect the cost decision-making, so it is difficult to do effective forecasting by using traditional methods such as time series approach, regression analysis. In this paper, a nonlinear model based on RBF neural network is presented. There are some ameliorated measures in leaning algorithm of radial basis function (RBF) neural...
In order to cope with the nonlinear and non-Gaussian time series, a RBF-HMM model, which is based on radial basis function (RBF) neural network with the assumption of measurement noise being hidden Markov model (HMM), is proposed in this paper. On the other hand, most of literatures about neural networks suppose that the number of input is invariable. Obviously, this assumption is improper in some...
In order to cope with nonlinear time series, a variable structure radial basis function (RBF) networks model, in which the numbers of basis functions and input order vary over time, is proposed in this paper. Then sequential Monte Carlo (SMC) method is used for time series on-line prediction and corresponding algorithm is developed. At last, the data of weekly price of the shipbuilding steel product...
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