This paper develops two dynamic neural network structures to forecast short-term railway passenger demand. The first neural network structure follows the idea of autoregressive model in time series forecasting and forms a nonlinear autoregressive model. In addition, two experiments are tested to eliminate redundant inputs and training samples. The second neural network structure extends the fir...

The subject of this paper is autoregressive AR approximations of a stationary Gaus sian discrete time process based on a nite sequence of observations We adopt the non parametric minimax framework and study how well can the process be approximated by a nite order autoregressive model Our results show that a properly chosen model dimen sion leads to an optimal in order minimax estimator

In this paper we propose a method for determining the number of regimes in threshold autoregressive models using smooth transition autoregression as a tool. As the smooth transition model is just an approximation to the threshold autoregressive one, no asymptotic properties are claimed for the proposed method. Tests available for testing the adequacy of a smooth transition autoregressive model ...

Burg estimators are classically used for the estimation of the autocovariance of a stationary autoregressive process. We propose to consider scale mixtures of stationary autoregressive processes, a non-Gaussian extension of the latter. The traces of such processes are Spherically Invariant Random Vectors (SIRV) with a constraint on the scatter matrix due to the autoregressive model. We propose ...

This paper studies least-squares parameter estimation algorithms for input nonlinear systems, including the input nonlinear controlled autoregressive IN-CAR model and the input nonlinear controlled autoregressive autoregressive moving average IN-CARARMA model. The basic idea is to obtain linear-in-parameters models by overparameterizing such nonlinear systems and to use the least-squares algori...

In this paper, we introduce an autoregressive model which has an evolution that is driven by an exogenous pilot signal. This model shares some properties with TAR (Threshold Auto Regressive) models and STAR (Smooth Transition Auto Regressive) models. This text de nes the model, it presents an estimator for this model, and an estimator for the variance of the innovation, which is not constant in...

In this paper, a hierarchical autoregressive model (HAR) method is proposed for image compression. The suggested techniques, looks at improving the compression ratio along with preserving the image quality by involving a multi-layered modeling concept. In comparison with traditional predictive coding or autoregressive model on a series of tested images it shows that the suggested method is bett...

We discuss computational aspects of likelihood-based estimation of univariate ARFIMA(p, d, q) models. We show how efficient computation and simulation is feasible, even for large samples. We also discuss the implementation of analytical bias corrections.