Triple seasonal autoregressive (TSAR) models have been introduced to model time series date with three layers of seasonality; however, the Bayesian identification problem these has not tackled in literature. Therefore, this paper, we objective filling gap by presenting a procedure identify best order TSAR models. Assuming that errors are normally distributed along employing priors, i.e., normal...

improving time series forecastingaccuracy is an important yet often difficult task.both theoretical and empirical findings haveindicated that integration of several models is an effectiveway to improve predictive performance, especiallywhen the models in combination are quite different. in this paper,a model of the hybrid artificial neural networks andfuzzy model is proposed for time series for...

The object of this paper is to study the asymptotic dependence structure of the linear time series models with infinitely divisible innovations by the use of their characteristic functions. Autoregressive moving-average (ARMA) models and fractional autoregressive integrated moving-average (FARIMA) models are analyzed. As examples of infinitely divisible innovations, the class of radially absolu...

In this paper we propose the Gaussian Dynamic Bayesian Smooth Transition Autoregressive (DBSTAR) models for nonlinear autoregressive time series processes as alternative to both the classical Smooth Transition Autoregressive (STAR) models of Chan and Tong (1986) and the computational Bayesian STAR (CBSTAR) models of Lopes and Salazar (2005). The DBSTAR models are autoregressive formulations of ...