Change point analysis in a state space framework to monthly temperature data in European cities

Abstract

In this work, we present time series of monthly average temperatures in several Euro- pean locations which were statistically analyzed using a state space approach, where it is considered a model with a deterministic seasonal component and a stochastic trend. The analysis of smoother prediction of the stochastic trend and its comparison in a tem- poral viewpoint can reveal patterns about warming in Europe. The temperature rise rates in Europe seem to have increased in the last decades when compared with longer periods, hence a change point detection method is applied to the trend component in order to identify these possible changes in the monthly temperature rise rates. The adopted methodology pointed out, for most series a change point in the late eighties.