Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression
Figure 11
Results related to real biogas data with different size of data sets. The first element is the reference element, which is assumed not to be an outlier. Results showed that the algorithm clearly identifies three regions as significant outliers (outliers from MMS), nonsignificant outliers (outliers from EMMS), and nonoutliers within each data segment. Most importantly, all the nonoutliers lied within a linear border, where red triangle corresponds to outliers detected by MMS, yellow circle corresponds to outliers detected by EMMS, and green square corresponds to nonoutliers. The value of k for MMS and EMMS is 0.2 and 0.1, respectively.
