Most emotion theories view emotions as reactions to situations in daily life. Process theories go further, proposing a feedback loop between environment, attention, emotion, and action that explains emotions’ adaptive role. Experience sampling data, which captures emotions in real time, should be ideal for testing such theories. However, existing emotion theories are largely verbal and lack precise predictions for these data. Here, we take a first step toward a generative model of emotion dynamics by formalizing the link between situations and emotions. This basic model already reproduces nine empirical phenomena in emotion time series, including temporal associations and distributional patterns. We then show how process theories can inform extensions of this model into a fuller generative account. Finally, we discuss how such models can support theory development, improve measurement, and guide study design and analysis. See the open access Psychological Review paper for details. The model and reproducibility materials are available here and here. For details have a look at the open access publication in Psychological Review. The model and the reproducibility archives are available here and here.