Nowadays, the availability of high-quality data such as smart meter data provides new challenges to the researchers. Such data can include extreme values due to meter malfunction, burst water pipes, etc. Therefore, special care must be given to these types of events in the series, and specific statistical procedures based on extremes' behaviour are required to handle them. Our aim is to model the statistical characteristics of such time series and understand extreme events' probabilities. The key ideas will be illustrated using hourly water consumption data from a water company in Portugal, Infraquinta.

Joint work with Yi He (University of Amsterdam) and Rob Hyndman (Monash University).