We discuss a method that analyzes time series generated by point processes to detect possible non stationarity in
the data. We interpret each observation as the first passage time of a stochastic process through a deterministic
boundary and we concentrate the effect of different dynamics on the boundary shape. We propose an estimator
for the boundary and we compute its confidence intervals. Applying the Inverse First Passage Time Algorithm
we then recognize the evolution in the dynamics of the time series by means of a comparison of the boundary
shapes. This is performed using a suitable time window fragmentation on the observed data.