This example illustrates logistic analysis of a data set with several components and masking. Figure 7 presents the results of an analysis of historical time-series data of French motorized mobility4. Periods of conflict, such as World War I, cause substantial deviations from normal activity; analysis of such trends, then, should focus on the years outside of these periods. That said, for this analysis, we excluded the data between 1912 and 1950.
We posit a logistic for the years of the Industrial Revolution, one for the advances in automotive production (i.e., the assembly line), and another for the post-WWII economic boom.
Figure 7a shows the data set D (circles) and the
estimated fitted curve
Accordingly, the weights for the corresponding data points were set to 0.
Figure 7b shows the component logistics normalized to their respective (for scale). Figure 7c shows the Fisher-Pry transform of the masked data set, while figure 7d shows the Fisher-Pry transform of all of the (unmasked) data.