This paper deals with off-line detection of change points for time series of
independent observations, when the number of change points is unknown. We
propose a sequential analysis like method with linear time and memory
complexity. Our method is based at first step, on Filtered Derivative method
which detects the right change points but also false ones. We improve Filtered
Derivative method by adding a second step in which we compute the p-values
associated to each potential change points. Then we eliminate as false alarms
the points which have p-value smaller than a given critical level. Next, our
method is compared with the Penalized Least Square Criterion procedure on
simulated data sets. Eventually, we apply Filtered Derivative with p-Value
method to segmentation of heartbeat time series
