Comparison of the Efficiency of Forecasting Techniques for Univariate Models with Seasonal Data
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Abstract
The objective of this research was to compare the efficiency of three forecasting techniques for univariate models with seasonal data. The three techniques: Seasonal Naïve Forecasting (SNF), Weighted Moving Average (WMA), and Winters’ Exponential Smoothing (WES) were used to construct forecasting models for the price of field corn in Tak province. The data gathered from Tak Provincial Agricultural Extension Office during January 2011 to December 2016. The forecast accuracies were compared by minimizing the Mean Absolute Percentage Error (MAPE), the Mean Absolute Deviation (MAD), and the Mean Squared
Deviation (MSD). Research finding indicated that among forecasting techniques had been studied, the most efficient technique was the Seasonal Naïve Forecasting (SNF), the Weighted Moving Average (WMA), and Winters’ Exponential Smoothing (WES), respectively.
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