Recently, there has been increased focus on sub-threshold stages of mental disorders, with attempts to model and predict progression to full-threshold disorder. Given this considerable research attention and clinical significance, it is timely to analyse the assumptions of theoretical models in the field. Research into predicting onset of mental disorder has shown an overreliance on one-off sampling of cross-sectional data (i.e., a "snapshot" of clinical state and other risk markers) and may benefit from taking dynamic changes into account. Cross-disciplinary approaches to complex system structures and changes, such as dynamical systems theory, network theory, instability mechanisms, chaos theory and catastrophe theory, offer potent models that can be applied to emergence (or decline) of psychopathology, including psychosis prediction and transdiagnostic symptom emergence. Staging provides a useful framework to research dynamic prediction in psychiatry. Psychiatric research may benefit from approaching psychopathology as a system rather than a category, identifying dynamics of system change (e.g., abrupt/gradual psychosis onset), factors to which these systems are most sensitive (e.g., interpersonal dynamics, neurochemical change), and individual variability in system architecture and change. The next generation of prediction studies may more accurately model the highly dynamic nature of psychopathology and system change, with treatment implications, such as introducing a means of identifying critical risk periods for mental state deterioration.