EXSMOOTH Subcommand (TSMODEL command)

The EXSMOOTH subcommand controls options for custom exponential smoothing models.

Using EXSMOOTH generates an error if independent variables are defined.
For seasonal models (SIMPLESEASONAL, WINTERSADDITIVE, and WINTERSMULTIPLICATIVE), an error is generated if the season length has not been defined via the DATE command or the SEASONLENGTH keyword.

Example

TSMODEL
  /AUXILIARY SEASONLENGTH=12
  /MODEL DEPENDENT=store1 TO store100 
  /EXSMOOTH TYPE=WINTERSMULTIPLICATIVE.

TYPE Keyword

The TYPE keyword is required and is used to specify the type of exponential smoothing model. There is no default method.

SIMPLE. Simple exponential smoothing model. This model is appropriate for series in which there is no trend or seasonality. Its only smoothing parameter is level. Simple exponential smoothing is most similar to an ARIMA model with zero orders of autoregression, one order of differencing, one order of moving average, and no constant.

SIMPLESEASONAL. Simple seasonal exponential smoothing model. This model is appropriate for series with no trend and a seasonal effect that is constant over time. Its smoothing parameters are level and season. Simple seasonal exponential smoothing is most similar to an ARIMA model with zero orders of autoregression, one order of differencing, one order of seasonal differencing, and orders 1, p, and p + 1 of moving average, where p is the number of periods in a seasonal interval (for monthly data, p = 12).

HOLT. Holt’s method. This model is appropriate for series in which there is a linear trend and no seasonality. Its smoothing parameters are level and trend, which are not constrained by each other's values. Holt's model is more general than Brown's model but may take longer to compute for large series. Holt's exponential smoothing is most similar to an ARIMA model with zero orders of autoregression, two orders of differencing, and two orders of moving average.

BROWN. Brown’s method. This model is appropriate for series in which there is a linear trend and no seasonality. Its smoothing parameters are level and trend, which are assumed to be equal. Brown's model is therefore a special case of Holt's model. Brown's exponential smoothing is most similar to an ARIMA model with zero orders of autoregression, two orders of differencing, and two orders of moving average, with the coefficient for the second order of moving average equal to the square of one-half of the coefficient for the first order.

DAMPEDTREND. Damped trend method. This model is appropriate for series with a linear trend that is dying out and with no seasonality. Its smoothing parameters are level, trend, and damping trend. Damped exponential smoothing is most similar to an ARIMA model with 1 order of autoregression, 1 order of differencing, and 2 orders of moving average.

WINTERSADDITIVE. Winters’ additive method. This model is appropriate for series with a linear trend and a seasonal effect that does not depend on the level of the series. Its smoothing parameters are level, trend, and season. Winters' additive exponential smoothing is most similar to an ARIMA model with zero orders of autoregression, one order of differencing, one order of seasonal differencing, and p + 1 orders of moving average, where p is the number of periods in a seasonal interval (for monthly data, p = 12).

WINTERSMULTIPLICATIVE. Winters’ multiplicative method. This model is appropriate for series with a linear trend and a seasonal effect that depends on the level of the series. Its smoothing parameters are level, trend, and season. Winters' multiplicative exponential smoothing is not similar to any ARIMA model.

TRANSFORM Keyword

The TRANSFORM keyword specifies a transformation that is performed on each dependent variable before it is modeled. By default, dependent variables are not transformed.

NONE. Dependent variable series are not transformed. This setting is the default.

SQRT. A square root transformation is performed.

LN. A natural log transformation is performed.