Comparison of Exponential Smoothing Models for Forecasting Cassava Production

Abstract

This paper evaluated and compared the performance of a family of smoothing models such as Simple Exponential Smoothing (SES), Holt's Linear Trend (HLT), Exponential trend (ET) and Holt's Damped Methods: additive and multiplicative; to forecast the annual Cassava production in Nigeria. The predictive capabilities were compared in terms of Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Percentage Error (MPE) and Mean Absolute Percentage Error (MAPE) based on the validated data set. The Holt's Exponential Trend with parameter was found to have best described the data having the lowest ranked error statistics in an out of sample performance.