This chapter gives an overview of time series analysis, which is necessary to understand event detection methods. Section 2.1 covers time series components. Section 2.1 formalizes the concept of stationarity in time series. Section 2.3 presents the major time series preprocessing techniques, including temporal aggregation, extraction of trend components, time series variance stabilization, detrending and differencing, time series decomposition, sliding windows, data normalization, and data splitting. Finally, Sect. 2.4 describes the basics of time series prediction using statistics and MLML principles.