Hands On Time Series Modeling of Rare Events, with Python

Modeling rare or extreme events within a time series can provide valuable insights beyond simply treating them as outliers. Using historical weather temperature data as a practical example, a process is detailed for identifying these extreme values and understanding their behavior. The methodology involves fitting specialized statistical distributions, including the Generalized Extreme Value (GEV), Weibull, and Gumbel distributions, to the rare event data. Model selection is then performed using metrics like the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) to determine the best fit.