Fits a general trend model to time series data. Choose between the linear, quadratic, exponential growth or decay, and S-curve trend models. Use this procedure to fit trend when there is no seasonal component in your series.
Smoothes your data by averaging consecutive observations in a series. You can use this procedure when your data do not have a trend component. If you have a seasonal component, set the length of the moving average to equal the length of the seasonal cycle.
Smoothes your data using the optimal one-step ahead ARIMA (0,1,1) forecasting formula. This procedure works best without a trend or seasonal component. The single dynamic component in a moving average model is the level.
Smoothes your data using the optimal one-step-ahead ARIMA (0,2,2) forecasting formula. This procedure can work well when there is a trend but it can also serve as a general smoothing method. Double Exponential Smoothing calculates dynamic estimates for two components: level and trend.