This study aims to determine an automatic forecasting method of univariate time series, using the nonlinear autoregressive neural network model with exogenous input (NARX). In this setting, users only need supply series. Then, algorithm sets up appropriate features, estimate parameters in model, and calculate forecasts, without users’ intervention. The used include preprocessing, tests for tren...

Although many macroeconomic time series are assumed to follow nonlinear processes, nonlinear models often do not provide better predictions than their linear counterparts. Furthermore, such models easily become very complex and difficult to estimate. The aim of this study is to investigate whether simple nonlinear extensions of autoregressive processes are able to provide more accurate forecast...

G13AFF is an easy-to-use version of G13AEF. It fits a seasonal autoregressive integrated moving average (ARIMA) model to an observed time series, using a nonlinear least-squares procedure incorporating backforecasting. Parameter estimates are obtained, together with appropriate standard errors. The residual series is returned, and information for use in forecasting the time series is produced f...

This paper proposes a constrained nonlinear programming view of generalized autoregressive conditional heteroskedasticity (GARCH) volatility estimation models in financial econometrics. These models are usually presented to the reader as unconstrained optimization models with recursive terms in the literature, whereas they actually fall into the domain of nonconvex nonlinear programming. Our re...

A new method for the identification of the nonlinear Hammerstein Model consisting a static nonlinearity in cascade with a linear dynamic part, is introduced. The static nonlinearity is modeled by radial basis function neural networks (RBFNN) and the linear part is modeled by an autoregressive moving average (ARMA) model. A recursive algorithm is developed to update the weights of the RBFNN and ...

This paper uses a Threshold Autoregressive (TAR) model with exogenous variables to explain a change in regime in Brazilian nominal interest rates. By using an indicator of currency crises the model tries to explain the difference in the dynamics of nominal interest rates during and out of a currency crises. The paper then compares the performance of the nonlinear model to a modified Taylor Rule...

In this paper variable bit rate VBR Moving Picture Experts Group (MPEG) coded full-motion video traffic is modeled by a nonlinear time-series process. The threshold autoregressive (TAR) process is of particular interest. The TAR model is comprised of a set of autoregressive (AR) processes that are switched between amplitude sub-regions. To model the dynamics of the switching between the sub-reg...

The inference of causal interaction structures in multivariate systems enables a deeper understanding of the investigated network. Analyzing nonlinear systems using partial directed coherence requires high model orders of the underlying vector-autoregressive process. We present a method to overcome the drawbacks caused by the high model orders. We calculate the corresponding statistics and prov...

 We consider the problem of model selection in vector autoregressive model with Normal innovation. Tests such as Vuong's and Cox's tests are provided for order and model selection, i.e. for selecting the order and a suitable subset of regressors, in vector autoregressive model. We propose a test as a modified log-likelihood ratio test for selecting subsets of regressors. The Europe oil prices, ...

Nowadays, there exist various methods for modelling and forecasting foreign exchange (FX) rates including economical models, statistical methods and learning neural networks. Dealing with the problems of nonstationarity and nonlinearity has been a challenge. In this paper, we propose a combined neural model for effectively tackling the problems. The model is termed as neural gas mixture of auto...