The problem considered in this paper is how to find reliable prediction intervals with simple exponential smoothing and trend corrected exponential smoothing. Methods for constructing prediction intervals based on linear approximation and bootstrapping are proposed. A Monte Carlo simulation study, in which the proposed methods are compared, indicates that the most reliable intervals can be obtained with a parametric form of the bootstrap method. An application of the method to predicting Malaysian GNP per capita is considered
The main objective of this paper is to provide analytical expressions for forecast variances that ca...
In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. Th...
The article demonstrates how the distribution-free method of bootstrapping can be applied to the con...
The problem considered in this paper is how to find reliable prediction intervals with simple expone...
Three general classes of state space models are presented, using the single source of error formulat...
Economic forecasting techniques are being successfully applied to problems of inventory and producti...
We construct bootstrap prediction intervals for linear autoregressions, nonlinear autoregressions, n...
This thesis deals with the use of statistical state space models of exponential smooth- ing for esti...
In the business world, it takes a prediction or estimate of an action that will be processed to foll...
Simple forecasting methods, such as exponential smoothing, are very popular in business analytics. T...
In order to construct prediction intervals without the combersome--and typically unjustifiable--assu...
The Holt's linear exponential smoothing method has been frequently used to forecast a time series th...
A new method is proposed to obtain interval forecasts for autoregressive models taking into account ...
In the motion picture industry, the movie market players always rely on accurate demand forecasts. D...
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on...
The main objective of this paper is to provide analytical expressions for forecast variances that ca...
In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. Th...
The article demonstrates how the distribution-free method of bootstrapping can be applied to the con...
The problem considered in this paper is how to find reliable prediction intervals with simple expone...
Three general classes of state space models are presented, using the single source of error formulat...
Economic forecasting techniques are being successfully applied to problems of inventory and producti...
We construct bootstrap prediction intervals for linear autoregressions, nonlinear autoregressions, n...
This thesis deals with the use of statistical state space models of exponential smooth- ing for esti...
In the business world, it takes a prediction or estimate of an action that will be processed to foll...
Simple forecasting methods, such as exponential smoothing, are very popular in business analytics. T...
In order to construct prediction intervals without the combersome--and typically unjustifiable--assu...
The Holt's linear exponential smoothing method has been frequently used to forecast a time series th...
A new method is proposed to obtain interval forecasts for autoregressive models taking into account ...
In the motion picture industry, the movie market players always rely on accurate demand forecasts. D...
The calculation of interval forecasts for highly persistent autoregressive (AR) time series based on...
The main objective of this paper is to provide analytical expressions for forecast variances that ca...
In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. Th...
The article demonstrates how the distribution-free method of bootstrapping can be applied to the con...