  Change points in real-world systems mark significant regime shifts in system
dynamics, possibly triggered by exogenous or endogenous factors. These points
define regimes for the time evolution of the system and are crucial for
understanding transitions in financial, economic, social, environmental, and
technological contexts. Building upon the Bayesian approach introduced in
\cite{c:07}, we devise a new method for online change point detection in the
mean of a univariate time series, which is well suited for real-time
applications and is able to handle the general temporal patterns displayed by
data in many empirical contexts. We first describe time series as an
autoregressive process of an arbitrary order. Second, the variance and
correlation of the data are allowed to vary within each regime driven by a
scoring rule that updates the value of the parameters for a better fit of the
observations. Finally, a change point is detected in a probabilistic framework
via the posterior distribution of the current regime length. By modeling
temporal dependencies and time-varying parameters, the proposed approach
enhances both the estimate accuracy and the forecasting power. Empirical
validations using various datasets demonstrate the method's effectiveness in
capturing memory and dynamic patterns, offering deeper insights into the
non-stationary dynamics of real-world systems.
