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In this paper we present the Radial Basis Neural Network Function. We examine some simple numerical examples of time-series in economics and finance. The forecasting performance is significant superior, especially in financial time-series, to traditional econometric modeling indicating that...
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The study proposes and a family of regime switching GARCH neural network models to model volatility. The proposed MS-ARMA-GARCH-NN models allow MS type regime switching in both the conditional mean and conditional variance for time series and further augmented with artificial neural networks to...
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Stock trading has tremendous importance not just as a profession but also as an income source for individuals. Many investment account holders use the appreciation of their portfolio (as a combination of stocks or indexes) as income for their retirement years, mostly betting on stocks or indexes...
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Industrial forecasting has entered an era of unprecedented growth in the size and complexity of data which require new modeling methodologies. While many new general purpose machine learning approaches have emerged, they remain poorly understand and irreconcilable with more traditional...
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The objective of this paper is to compare different forecasting methods for the short run forecasting of Business Survey Indicators. We compare the forecasting accuracy of Artificial Neural Networks (ANN) vs. three different time series models: autoregressions (AR), autoregressive integrated...
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