An Improvement Single Exponential Smoothing Method for Forecasting in Time Series

In this paper we describe single exponential smoothing method, which is used in time series forecasting, and suggest an improving to the single exponential smoothing method through adding the mean of the first differences for the time series for all predicting values of the single exponential smoothing. The improved method was compared with single exponential smoothing method by using real time series data for wheat national production for the period (1961-2002) through depending on Cumulative Forecasting Error (CFE), Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE) as criteria for comparison. It is clear that the improv method was more efficient than the single exponential smoothing method for forecasting in time series.