The aim of this short-course is to provide an introduction to and overview of key techniques of time-series analysis to identify and quantify - and perhaps predict - natural hazards. It will take key questions such as 'Are we looking for cyclic or anomalous phenomena?' as its starting point: time-series analysis can be used to identify both cyclic and temporally anomalous events.

Many natural hazards have cyclic/periodic features, e.g. radon, earthquakes (under some circumstances), (seasonal) droughts and floods, whereas others are anomalous, apparently random with regard to time, e.g. earthquakes (under most circumstances). For the cyclic cases, an analysis of past time-series can yield an expectation and perhaps some degree of forecasting, even if only at the level of 'more probable' and 'less probable' times. For anomalous events, investigation of past time-series might reveal evidence of precursory events which again might permit some degree of forecasting, even if only at the level of 'more probable' and 'less probable' times.

The focus will be on key Fourier and related techniques, i.e. Fourier transforms and auto- and cross- correlation. Anticipating that most of those who attend will be 'users' rather than 'developers', emphasis will be given to the interpretation of the output.