Predicting Mood in College Students: Developing a Predictive Model From Multivariate Time Series

Abstract

Sleep diaries often collect useful information regarding students’ sleep duration, timing, moods, and relevant daytime activities. The abundance of data provided by these multivariate time series provide a basis by which to carry out predictions for end-of-month results. In particular, end-of-month moods are interesting to predict since they can be indicators of larger health problems, such as depression or anxiety. This paper attempts to model the clusters students fall into based on sleep variables and the time-dependent network that contributes to end-of-month mood ratings in an attempt to find important variables on certain days to target for treatment. It concludes by finding that dependent on the cluster a student falls into, wake time, first event timing, or biological determinants are most important in predicting 28th day moods.