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 new class of models for data showing trend and multiplicative seasonality is presented. The models allow the forecast error variance to depend on the trend and/ or the seasonality. It can be shown that each of these models has the same updating equations and forecast functions as the...

The focus of this paper is on the relationship between the exponential smoothing methods of forecasting and the integrated autoregressive-moving average models underlying them. In this paper we derive, for the first time, the general linear relationship between their parameters. A method,...

A parsimonious method of exponential smoothing is introduced for time series generated from a combination of local trends and local seasonal effects. It is compared with the additive version of the Holt-Winters method of forecasting on a standard collection of real time series.

The local linear trend and global linear trend models embody extreme assumptions about trends. According to the local linear trend formulation the level and growth rate are allowed to rapidly adapt to changes in the data path. On the other hand, the Glaobal linear trend model makes no allowance...

A new simple formula is found to correct the underestimation of the standard deviation for total lead time demand when using simple exponential smoothing. This new formula allows one to see readily the significant size of the underestimation of the traditional formula and can easily be...

In this paper, a Bayesian version of the exponential smoothing method of forecasting is proposed. The approach is based on a state space model containing only a single source of error for each time interval. This model allows us to improve current practices surrounding exponential smoothing by...

The problem of constructing prediction intervals for linear time series (ARIMA) models is examined. The aim is to find prediction intervals which incorporate an allowance for sampling error associated with parameter estimates. The effect of constraints on parameters arising from stationary and...