Time-series analysis allows us to investigate trends over time at a population level and how two or more series co-vary, as well as the impact of population level policies and interventions. Several analyses are available which take into account seasonality and autocorrelation (where measurements taken closer in time are more similar). One popular analysis is ARIMA (and ARIMAX) modelling. A major assumption of these is that of stationarity which means that the mean, variance and autocorrelation structure do not change over time. For this to be achieved, underlying trends must be removed through what is known as differencing (and perhaps a transformation). Differencing involves using the difference between a value and the one for the time period immediately previous to it. Example data is shown in the graphs above before and after differencing. ARIMA then assesses how one series changes over time as a function of another without confounding from any underlying changes.