Single Exponential Smoothing overview how to example data see also
Single Exponential Smoothing Stat > Time Series > Single Exp Smoothing
Single exponential smoothing smoothes your data by computing exponentially weighted averages and provides short-term forecasts. This procedure works best for data without a trend or seasonal component. See [1], [4], and [6] for a discussion of exponential smoothing methods.
Variable: Enter the column containing the time series.
Weight to Use in Smoothing: Use the options below to specify which weight to use. See Computing Weights, or Smoothed Values for details.
Optimal ARIMA: Choose to use the default weight, which Minitab computes by fitting an ARIMA (0, 1, 1) model to the data. With this option, Minitab calculates the initial smoothed value by backcasting.
Use: Choose to enter a specific weight, then type a number greater than or equal to 0 and less than 2. With this option, Minitab uses the average of the first six observations (or all the observations if there are less than six observations) for the initial smoothed value by default. You can change this default by specifying a different value in the Single Exponential Smoothing - Options dialog box.
Generate forecasts: Check to generate forecasts. Forecasts appear in green on the time series plot with 95% prediction interval bands.
Number of forecasts: Enter an integer to indicate how many forecasts you want.
Starting from origin: Enter a positive integer to specify a starting point for the forecasts. For example, if you specify 4 forecasts and 48 for the origin, Minitab computes forecasts for periods 49, 50, 51, and 52, based on the smoothed value at period 48. If you leave this space blank, Minitab generates forecasts from the end of the data.