A Comparison of the Smoothing Constant Values Among Exponential Smoothing Methods in Commodity Prices Forecasting

Forecasting with exponential smoothing methods and bootstrap

The Boot.EXPOS procedure is an algorithm that combines the use of exponential smoothing methods with the bootstrap methodology for obtaining forecasts. In previous works the authors have studied and analyzed the interaction between these two methodologies. The initial sketch of the procedure was developed, modified and evaluated until its final form designated as Boot.EXPOS.

A Comparison Between Time Series, Exponential Smoothing, and Neural Network Methods To Forecast GDP of Iran

Forecasting Compositional Time Series with Exponential Smoothing Methods

Compositional time series are formed from measurements of proportions that sum to one in each period of time. We might be interested in forecasting the proportion of home loans that have adjustable rates, the proportion of nonagricultural jobs in manufacturing, the proportion of a specific oxide in the geochemical composition of a rock, or the proportion of an election betting market choosing a...

Volatility Forecasting with Smooth Transition Exponential Smoothing

Adaptive exponential smoothing methods allow smoothing parameters to change over time, in order to adapt to changes in the characteristics of the time series. This paper presents a new adaptive method for predicting the volatility in financial returns. It enables the smoothing parameter to vary as a logistic function of user-specified variables. The approach is analogous to that used to model t...

Forecasting for Inventory Control with Exponential Smoothing