Database Modeling and Design

180 CHAPTER 8 Business Intelligence
Figure 8.17
Least squares line (courtesy of Ubiquiti, Inc.)
tial smoothing, based on the same data set used to compute Figure 8.17.
Notice the prediction is now more tightly bound to recent history.
Triple exponential smoothing models three components: level,
trend, and seasonality. This is more sophisticated than double exponential
smoothing, and gives better predictions when the data does indeed
exhibit seasonal behavior. Figure 8.19 shows the predictions made by triple
exponential smoothing, based on the same data used to compute
Figures 8.17 and 8.18. Notice the prediction intervals are tighter than in
Figures 8.17 and 8.18. This is a sign that the data varies seasonally; triple
exponential smoothing is a good model for the given type of data.
Exactly how reliable are these predictions? If we revisit the predictions
after time has passed and compare the predictions with the actual
values, are they accurate? Figure 8.20 shows the actual data overlaid
with the predictions made in Figure 8.19. Most of the actual data points
do indeed lie within the prediction intervals. The prediction intervals
look very reasonable. Why don’t we use these forecast models to make
our millions on Wall Street? Take a look at Figure 8.21, a cautionary tale.
Figure 8.21 is also based on the triple exponential smoothing model,
using four years of known data for training, compared with five years of
data used in constructing the model for Figure 8.20. The resulting pre-