We analyze the effects on prediction intervals of fitting ARIMA models to series with stochastic trends, when the underlying components are heteroscedastic. We show that ARIMA prediction intervals may be inadequate when only the transitory component is heteroscedastic. In this case, prediction intervals based on the unobserved component models tend to the homoscedastic intervals as the predicti...

This paper reexamines the time series properties of the US ex post real interest rate. The estimation of the ARFIMA model using the Conditional Sum of Squares (CSS) method reveals that the ex post real interest rate can be well described using a fractionally integrated process.  2000 Elsevier Science S.A. All rights reserved.

The ability to create forecasts and discover trends is a value to almost any industry. The challenge comes in finding the right data and the appropriate tools to analyze and model such data. This paper aims to demonstrate that it may be possible to create technology forecasting models through the use of patent groups. The focus will be on applying time series modeling techniques to a collection...

This paper discusses three modelling techniques, which apply to multiple time series data that correspond to different spatial locations (spatial time series). The first two methods, namely the Space-Time ARIMA (STARIMA) and the Bayesian Vector Autoregressive (BVAR) model with spatial priors apply when interest lies on the spatio-temporal evolution of a single variable. The former is better sui...

A state–space approach provides a general unified framework for calculation of the Beveridge–Nelson decomposition for a wide variety of time series models, including all univariate and vector ARIMA models.  2002 Elsevier Science B.V. All rights reserved.

This paper works on the agricultural drought forecasting in the Guanzhong Plain of China using Autoregressive Integrated Moving Average (ARIMA) models based on the time series of drought monitoring results of Vegetation Temperature Condition Index (VTCI). About 90 VTCI images derived from Advanced Very High Resolution Radiometer (AVHRR) data were selected to develop the ARIMA models from the er...

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...