## 4 Results
V. The PCA was performed to determine the main features influencing the series classifications. First, the eigenvalues were computed together with the percentage of the variance explained by each and the percentage of the cumulative variance. Two methods were used to select the number of the principal components (PCs) to be retained: (a) keep the components whose eigenvalues are greater than one, and (b) keep the components until the cumulative percentage variance explained is high enough. For Zone 1, we can keep either the first three PCs (the eigenvalues are greater than one) that explain 78.52% of the variance or the first four components, which explain 87.28%. For the second zone, we can keep the first three PCs (with the corresponding eigenvalues 4.3845, 3.2678, and 1.7975) that explain 85.908% of the variance or the first four, explaining 92.706 of it. Zone 3's first four components have eigenvalues greater than 1, explaining 90.4125% of the variance. The scree plots containing the percentage of explained variance by each PC are presented in Figure 13.  
For Zone 1, Figure 14a shows the contributions of the variables on the first two dimensions in colors, with the lowest contributions in turquoise and the highest in dark orange. The contributions of the features on the third dimension are presented in Figure 14b. For example, the highest contribution on the first dimension is that of F2 (maximum), followed by F4 (standard deviation) and F6 (kurtosis). The highest contributions on the second dimension are those of F9 (ACF), F10 (PACF), and F7 (normality), whereas on the third one--F7 (normality), F8 (homoskedasticity), and F1 (minimum). Summing up, it results that the main contributions on the first two dimensions are those of F2 (maximum), F4 (standard deviation), F9 (ACF), and F10 (PACF) (Figure 15a), whereas on all three dimensions, F9, F10, F2, and F4 (Figure 15b). The quality of representation for variables on the factor map is called cos2. It is displayed in Figure 16 for Zone, with different dimensions of the bullets and colors depending on the intensity. The highest contributions on dimension 1 are those of Sites 14, 9, 13 (on the positive direction of the first axis--belong to the first cluster in Figure 12a), and 11, 10 (on the negative direction of the first axis--belong to the second cluster in Figure 12a. Figures S6 and S7 present the detailed contributions of the variables (features) in Zones 2 and 3 on different dimensions. Figure 17 shows the contributions of each variable on the first 5 dimensions and the biplots. We summarize the findings below. Figure 12: Clusters of the features’ series in (**a**) Zone 1, (**b**) Zone 2, (**c**) Zone 3. Figure 14: (a) Variables PCA, (b) Contributions of the variables to the third dimension. Figure 13: Screeplot for (a) Zone 1, (b) Zone 2, (c) Zone 3. For Zone 2:
* The main contributions on Dim-1 are those of F3, F1, F2, and F4;
* The main contributions on Dim-2 are those of F10, F12, F7, and F9 (i.e., PACF, outliers, normality, and ACF);
* The main contributions on Dim-1-2 are those of the average (F3), maximum (F2), minimum (F1), and PACF (F10);
* The most important contributions on Dim-1-2 are those of the average (F3), maximum (F2), standard deviation (F4), and skewness (F5);
* The most important contributions on Dim-1-2-3 are those of the average (F3), maximum (F2), standard deviation (F4), and skewness (F5);
* Considering the locations, the most important contributions on the dimensions are those of 3, 2 (on the positive direction of the first one), 5 and 4 (on the negative direction of the first one), 8 and 9 (on the positive direction of the second one), and 6 and 11 (on the negative direction of the second one).