| The objective of this paper is to demonstrate the limitations of the variance-time (VT) test as a statistical tool for inferring long-range dependence (LRD) in network traac. Since the early Bellcore studies 7], 16], LRD has been in the center of a continuous debate within the teletraf-c community. The controversy is typically focused on the utility of LRD models to predict the performance at...

Forecasting accuracy is one of the most favorable critical issues in Autoregressive Integrated Moving Average (ARIMA) models. The study compares the application of two forecasting methods on the amount of Taiwan export, the Fuzzy time series method and ARIMA method. Model discussed for the ARIMA method and Fuzzy time series method include the Sturges rules. When the sample period is extend in o...

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...

We study asymptotic properties of the local Whittle estimator of the long memory parameter for a wide class of fractionally integrated nonlinear time series models+ In particular, we solve the conjecture posed by Phillips and Shimotsu ~2004, Annals of Statistics 32, 656–692! for Type I processes under our framework, which requires a global smoothness condition on the spectral density of the sho...

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...

This paper discusses the application of space–time autoregressive integrated moving average (STARIMA) methodology for representing traffic flow patterns. Traffic flow data are in the form of spatial time series and are collected at specific locations at constant intervals of time. Important spatial characteristics of the space–time process are incorporated in the STARIMA model through the use o...

Numerous time series admit weak autoregressive-moving average (ARMA) representations, in which the errors are uncorrelated but not necessarily independent nor martingale differences. The statistical inference of this general class of models requires the estimation of generalized Fisher information matrices. We give analytic expressions and propose consistent estimators of these matrices, at any...