This example teaches you how to calculate the exponential smoothing of a time series or other kind of data series.
This is used to smooth out irregularities (peaks and valleys) to easily recognize trends.
First, let's take a look at our time series:
Obviously it has a big seasonal feature, peaks and valleys can be easily recognized.
1. Select "Exponential Smoothing" and click OK.
2. In the Input Range select B4:B55.
3. In the Damping factor cell write a number between 0 and 1; for example 0,8.
Literature often talks about the smoothing constant Î± (alpha). The value (1- Î±) is called the damping factor.
4. For Output Range select C4.
5. Click OK.
The new, red line represents the exponentially smoothed series. As a result, peaks and valleys are smoothed out. The graph is revealing a slightly increasing trend.
Explanation: because we set damping factor = 0,8, thus alpha to 0.2, the previous data point is given a relatively small weight while the previous smoothed value is given a large weight (i.e. 0.9). As a result, peaks and valleys are smoothed out. The graph shows an increasing trend. The smaller alpha (larger the damping factor), the more the peaks and valleys are smoothed out. The larger alpha (smaller the damping factor), the closer the smoothed values are to the actual data points.