A time series is a sequence of observations. Time series events are instants or intervals in the time series where observations change in a manner that is considered important for analysis or decision-making. Event detection is the process of discovering when these events occur in a time series. This chapter introduces the principles associated with event detection, which we organize in a general taxonomy. Section 1.1 gives an overview of time series events. Section 1.2 characterizes the main different types of events, including anomalies, change points, and motifs. Section 1.3 provides the general data structure adopted to perform time series event detection. Time series event detection involves searching for events through event detection methods. Thus, we cover detection methods, detection scenarios, and detection evaluation in Sects. 1.4, 1.5, and 1.6, respectively. Section 1.7 introduces the book structure.