 6. Process or Product Monitoring and Control
6.4. Introduction to Time Series Analysis
6.4.3. What is Exponential Smoothing?

## Double Exponential Smoothing

Double exponential smoothing uses two constants and is better at handling trends As was previously observed, Single Smoothing does not excel in following the data when there is a trend. This situation can be improved by the introduction of a second equation with a second constant, $$\gamma$$, which must be chosen in conjunction with $$\alpha$$.

Here are the two equations associated with Double Exponential Smoothing. $$\begin{eqnarray} S_t & = & \alpha y_t + (1 - \alpha)(S_{t-1} + b_{t-1}) & & 0 \le \alpha \le 1 \\ & & \\ b_t & = & \gamma(S_t - S_{t-1}) + (1 - \gamma) b_{t-1} & & 0 \le \gamma \le 1 \end{eqnarray}$$ Note that the current value of the series is used to calculate its smoothed value replacement in double exponential smoothing.

Initial Values
Several methods to choose the initial values As in the case for single smoothing, there are a variety of schemes to set initial values for $$S_t$$ and $$b_t$$ in double smoothing.

$$S_1$$ is in general set to $$y_1$$. Here are three suggestions for $$b_1$$. $$\begin{eqnarray} b_1 & = & y_2 - y_1 \\ & & \\ b_1 & = & \frac{1}{3} \left[ (y_2 - y_1) + (y_3 - y_2) + (y_4 - y_3) \right] \\ & & \\ b_1 & = & \frac{y_n - y_1}{n-1} \end{eqnarray}$$

Meaning of the smoothing equations The first smoothing equation adjusts $$S_t$$ directly for the trend of the previous period, $$b_{t-1}$$, by adding it to the last smoothed value, $$S_{t-1}$$. This helps to eliminate the lag and brings $$S_t$$ to the appropriate base of the current value.
Non-linear optimization techniques can be used The values for $$\alpha$$ and $$\gamma$$ can be obtained via non-linear optimization techniques, such as the Marquardt Algorithm. 