  Active regions on the photosphere of a star have been the major obstacle for
detecting Earth-like exoplanets using the radial velocity (RV) method. A
commonly employed solution for addressing stellar activity is to assume a
linear relationship between the RV observations and the activity indicators
along the entire time series, and then remove the estimated contribution of
activity from the variation in RV data (overall correction method). However,
since active regions evolve on the photosphere over time, correlations between
the RV observations and the activity indicators will correspondingly be
anisotropic. We present an approach that recognizes the RV locations where the
correlations between the RV and the activity indicators significantly change in
order to better account for variations in RV caused by stellar activity. The
proposed approach uses a general family of statistical breakpoint methods,
often referred to as change point detection (CPD) algorithms; several
implementations of which are available in R and python. A thorough comparison
is made between the breakpoint-based approach and the overall correction
method. To ensure wide representativity, we use measurements from real stars
that have different levels of stellar activity and whose spectra have different
signal-to-noise ratios. When the corrections for stellar activity are applied
separately to each temporal segment identified by the breakpoint method, the
corresponding residuals in the RV time series are typically much smaller than
those obtained by the overall correction method. Consequently, the generalized
Lomb-Scargle periodogram contains a smaller number of peaks caused by active
regions. The CPD algorithm is particularly effective when focusing on active
stars with long time series, such as alpha Cen B.
