In this paper we apply the strategy of trend-damping to the popular Winters exponential smoothing systems for seasonal time series. Efficient model formulations are derived for both multiplicative and additive seasonal patterns. An algorithm is given to test the stability of the models in cases where predetermined smoothing parameters are used. Empirical results are presented to show that trend-damping improves ex ante forecast accuracy in seasonal data, especially at long leadtimes.forecasting: time series
Multiplicative trend exponential smoothing has received very little attention in the literature. It ...
This paper discusses the instability of eleven nonlinear state space models that underly exponential...
Over the past twenty years, damped trend exponential smoothing has performed well in numerous empiri...
The characteristics of seasonally adjusted, exponentially smoothed forecasts are studied through the...
A parsimonious method of exponential smoothing is introduced for time series generated from a combin...
Simple forecasting methods, such as exponential smoothing, are very popular in business analytics. T...
A method of adjusting for seasonality and trends is demonstrated using general merchanise retail dat...
New innovations state space modeling tools, incorporating Box-Cox transformations, Fourier series wi...
This study deals with forecasting economic time series that have strong trends and seas...
Forecasting using time series (TS) models are often based on linear regression or methods using vari...
Title: Methods for periodic and irregular time series Author: Mgr. Tomáš Hanzák Department: Departme...
The focus of this paper is on the relationship between the exponential smoothing methods of forecast...
summary:The paper suggests a generalization of widely used Holt-Winters smoothing and forecasting me...
Why the damped trend works The damped trend method of exponential smoothing is a benchmark that has ...
Abstract: Robust versions of the exponential and Holt-Winters smoothing method for forecasting are p...
Multiplicative trend exponential smoothing has received very little attention in the literature. It ...
This paper discusses the instability of eleven nonlinear state space models that underly exponential...
Over the past twenty years, damped trend exponential smoothing has performed well in numerous empiri...
The characteristics of seasonally adjusted, exponentially smoothed forecasts are studied through the...
A parsimonious method of exponential smoothing is introduced for time series generated from a combin...
Simple forecasting methods, such as exponential smoothing, are very popular in business analytics. T...
A method of adjusting for seasonality and trends is demonstrated using general merchanise retail dat...
New innovations state space modeling tools, incorporating Box-Cox transformations, Fourier series wi...
This study deals with forecasting economic time series that have strong trends and seas...
Forecasting using time series (TS) models are often based on linear regression or methods using vari...
Title: Methods for periodic and irregular time series Author: Mgr. Tomáš Hanzák Department: Departme...
The focus of this paper is on the relationship between the exponential smoothing methods of forecast...
summary:The paper suggests a generalization of widely used Holt-Winters smoothing and forecasting me...
Why the damped trend works The damped trend method of exponential smoothing is a benchmark that has ...
Abstract: Robust versions of the exponential and Holt-Winters smoothing method for forecasting are p...
Multiplicative trend exponential smoothing has received very little attention in the literature. It ...
This paper discusses the instability of eleven nonlinear state space models that underly exponential...
Over the past twenty years, damped trend exponential smoothing has performed well in numerous empiri...