  In this article there is no intention to repeat basic concepts about risk
management, but we will try to define why often is usefull the time series
analysis during the assessment of risks, and how is possible to compute a
significative analysis using regression and autoregression. After some basic
concepts about trend analysis, will be introduced some methods to identify
peaks. This is often usefull when there is no need to use the full time series,
because sometimes is more practical to focus only on the extremes. With a
correct time series without not-anomalous data, the extremes time series are
treated with a simply autoregression model. This drives to know if the time
series has a correlation between periods, and how many periods could be
considered lagged among them. We think that climate events frequently are
lagged because the climate show a clear increasing tendency, and that climate
risks are potentially increasing during the time. There will be no specific
conclusion related with risk management, because the proposed solution with
autoregression can be adapted to any time series analysis.
