Kalman filters and ARIMA models provide optimum control and evaluation techniques (in a minimum squared error sense) for clocks and precision oscillators. Typically, before the models can be used, an analyeie of data provides estimates of the model parameters (e.g., the phi's and theta's for an ARIMA model). These model parameters are often evaluated in a batch mode on a computer after a large ...

OBJECTIVES To investigate the impact of Australia's plain tobacco packaging policy on two stated purposes of the legislation--increasing the impact of health warnings and decreasing the promotional appeal of packaging--among adult smokers. DESIGN Serial cross-sectional study with weekly telephone surveys (April 2006-May 2013). Interrupted time-series analyses using ARIMA modelling and linear ...

This article reviews the practical aspects of the use of ARIMA (autoregressive, integrated, moving average) modelling of time series as applied to the surveillance of reportable infectious diseases, with special reference to the widely available SSS1 package, produced by the Centers for Disease Control and Prevention. The main steps required by ARIMA modelling are the selection of the time seri...

As to the established gray model based on the linear time-variant and individual prediction model of ARIMA, this article constructs the combined forecasting model based on the gray model and the time series model by means of relative error weighing. This prediction indicates that both the gray model and ARIMA model exert efficient function on the Torpedo development cost prediction, and the com...

Traditionally, the autoregressivemoving average (ARMA)model has been one of themost widely used linear models in time series prediction. Recent research activities in forecasting with artificial neural networks (ANNs) suggest that ANNs can be a promising alternative to the traditional ARMA structure. These linear models and ANNs are often compared with mixed conclusions in terms of the superior...