This chapter begins by exploring the clues to time provided through linguistic information, and then examines the orders in which events can be narrated, before reviewing psychological experiments on human processing of temporal ordering in narrative. The chapter explains how to annotate times and temporal relations in stories, relying on an underlying AI reasoning formalism called the interval calculus. The annotation can also capture situations where times and events are remembered or imagined, leading to the use of subordination relations. While human annotators agree well on how to annotate the times using the formalism, they may disagree on what temporal relations hold between certain pairs of events. The chapter then discusses how machines can automatically understand the temporal structures in narratives, from short sequences to entire stories, also examining the performance of LLMs in this regard. After discussing how to infer narrative tempo (or pace) and estimating event durations in narratives, the chapter extends the NarrativeML annotation scheme to take these temporal facets into account.