Algorithm 1: Segmentation procedure based on Jeffreys distance

Data: Input data Divide data into M segments of length equal to L: Segment 1, Segment 2, …, Segment M Estimate the pdf in each segment: 1  for$k\leftarrow 1$toMdo 2  $\lfloor$ Calculate ${\widehat{p}}_k\left(x\right)$ for Segment k Estimate the pdf of the last L samples in the signal: ${\widehat{p}}_{*}\left(x\right)$ Calculate the Jeffreys distance: 3  for$k\leftarrow 1$toMdo 4  $\lfloor$ Calculate $\widehat{J}({\widehat{p}}_k\left(x\right),{\widehat{p}}_{*}\left(x\right))$ Calculate the increments of Jeffreys distance: 5  for$k\leftarrow 1$to$M-1$do 6  $\lfloor$ Calculate $D_k=\widehat{J}({\widehat{p}}_{k+1}\left(x\right),{\widehat{p}}_{*}\left(x\right))-\widehat{J}({\widehat{p}}_k\left(x\right),{\widehat{p}}_{*}\left(x\right))$ Use the LLR method to find change in scale in $\left(D_1,\dots ,D_{M-1}\right)$: $i^{*}$ Use the LLR method to find change in scale in $\left(D_1,\dots ,D_{i^{*}-1}\right)$: $i^{**}$ Use the LLR method to find change in scale in $\left(D_1,\dots ,D_{i^{*}-1}\right)$: $i^{***}$