In this Time Series model, the simple exponential smoothing forecasts are "enhanced" by a linear trend component that is smoothed independently via the g (gamma) parameter (see discussion of trend smoothing parameters). This model is also referred to as Holt's two parameter method. This model would, for example, be adequate when producing forecasts for spare parts inventories. The need for particular spare parts may slowly increase or decrease over time (the trend component), and the trend may slowly change as different machines etc. age or become obsolete, thus affecting the trend in the demand for spare parts for the respective machines.

In order to compute the smoothed value (forecast) for the first observation in the series, both estimates of S0 and T0 (initial trend) are necessary. By default, these values are computed as:

T0 = (Xn-X1)/(N-1)

where

N |
is the length of the series, |

and

S0 = X1-T0/2

See also, Nonseasonal, Damped Trend; Nonseasonal, Exponential Trend; and Nonseasonal, No Trend.