The choice of the appropriate degree of integration is a very important question in ARIMA time series modeling. This choice is particularly diicult in the presence of either a nearly nonstationary autoregression or a fractionally integrated process. Via a Monte Carlo study we assess the size and power of MA, AR and spectral estimation tests in the presence of fractionally integrated, nearly non...

The paper provides general matrix formulas for minimum mean squared error signal extraction, for a finitely sampled time series whose signal and noise components are nonstationary ARIMA processes. These formulas are quite practical; as well as being simple to implement on a computer, they make it possible to easily derive important general properties of the signal extraction filters. We also ex...

This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models the Box Jenkins approach and the objective penalty function methods. The emphasis is on forec...

Network traffic prediction plays a vital role in the optimal resource allocation and management in computer networks. This paper introduces an ARIMA based model for the real time prediction of VBR video traffic. The methodology presented here can successfully addresses the challenges in traffic prediction such as accuracy in prediction, resource management and utilization. ARIMA application on ...

Abstract. We consider the finite-past predictor coefficients of stationary time series, and establish an explicit representation for them, in terms of the MA and AR coefficients. The proof involves the alternate iteration of projection operators associated with the infinite past and the infinite future. We provide several applications, which include rates of convergence of the finite predictor ...

In this paper we propose a likelihood based ratio test to detect distributional changes in common teletraac models. These include traditional models like the Markov Modulated Poisson Process and processes exhibiting long range dependency, in particular Gaussian fractional ARIMA processes. A practical approach is also developed for the case where the parameter after the change is unknown. It is ...

In this article, we forecast crude oil and natural gas spot prices at a daily frequency based on two classification techniques: artificial neural networks (ANN) and support vector machines (SVM). As a benchmark, we utilize an autoregressive integrated moving average (ARIMA) specification. We evaluate outof-sample forecast based on encompassing tests and mean-squared prediction error (MSPE). We ...

Stochastic models that estimate the ground-level ozone concentrations in air at an urban and rural sampling points in South-eastern Spain have been developed. Studies of temporal series of data, spectral analyses of temporal series and ARIMA models have been used. The ARIMA model (1,0,0) x (1,0,1)24 satisfactorily predicts hourly ozone concentrations in the urban area. The ARIMA (2,1,1) x (0,1,...

This paper outlines the practical steps which need to be undertaken to use autoregressive integrated moving average (ARIMA) time series models for forecasting Irish inflation. A framework for ARIMA forecasting is drawn up. It considers two alternative approaches to the issue of identifying ARIMA models the Box Jenkins approach and the objective penalty function methods. The emphasis is on forec...