This paper presents a summary and a brief theoretical introduction to time series ARIMA modeling of single subject data. Time series, a statistical technique that may be appropriate when data are measured repeatedly and at nearly equal intervals of time, has potential research applications in the study of chronic diseases such as diabetes, hypertension, and herpes simplex. Both intervention models and multivariate models are covered, with examples illustrating the utility of time series techniques in chronic disease research. Time series modeling of a subject with diabetes before and after being placed on a regimen of chlorpropamide is used to demonstrate the potential of intervention analysis. Multivariate time series techniques are illustrated by modeling the relationship between exercise and blood glucose, and by modelling the relationship between psychosocial distress and lymphocyte subsets of the cellular immune system.