The underlying assumption is that those features generated by a user during the past day are most likely to have influenced her mood, resulting in the observed mood scores.
given a mood form completed by a certain user at time t, we focused on her past 24 hours before t, in order to extract our features from and aggregate these features in different time windows within the 24 hour period, and, more specifically, into 5 different windows (1, 6, 12, 18 and 24 hours before the completion of a mood form), to allow for an extra level of granularity to the effect of proximity to the mood form timestamp.