In this paper we present a Feed-Foward Neural Networks Autoregressive (FFNN-AR) model with genetic algorithms training optimization in order to predict the gross domestic product growth of six countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final optimum weights from inpu...

In this paper we present an autoregressive model with neural networks modeling and standard error backpropagation algorithm training optimization in order to predict the gross domestic product (GDP) growth rate of four countries. Specifically we propose a kind of weighted regression, which can be used for econometric purposes, where the initial inputs are multiplied by the neural networks final...

We describe a new model for learning meaningful representations of text documents from an unlabeled collection of documents. This model is inspired by the recently proposed Replicated Softmax, an undirected graphical model of word counts that was shown to learn a better generative model and more meaningful document representations. Specifically, we take inspiration from the conditional mean-fie...

Methods: Using daily exchange rates for 7 years (January 1, 2008, to April 30, 2015), this study attempted to model dynamics following generalized autoregressive conditional heteroscedastic (GARCH), asymmetric power ARCH (APARCH), exponential generalized autoregressive conditional heteroscedstic (EGARCH), threshold generalized autoregressive conditional heteroscedstic (TGARCH), and integrated g...

The assumption of Gaussian innovation terms in linear time series analysis is quite restrictive. Under this assumption, both the marginal and conditional distributions of the time series are Gaussian. However, in real life many time series display features which seem to violate the Gaussian assumption. For example, Chan and Tong (1998) show that the Canadian lynx data have a bimodal marginal di...

Although a myriad of methods have been advanced to tackle spatial and temporal structures in data separately, it becomes difficult to analyze these data using classical linear regression models when spatial-temporal structures coexist, especially when the data size is relatively large. In this article, we demonstrate a simple to implement method to handle spatial-temporal structures simultaneou...