  Through Ecological Momentary Assessment (EMA) studies, a number of
time-series data is collected across multiple individuals, continuously
monitoring various items of emotional behavior. Such complex data is commonly
analyzed in an individual level, using personalized models. However, it is
believed that additional information of similar individuals is likely to
enhance these models leading to better individuals' description. Thus,
clustering is investigated with an aim to group together the most similar
individuals, and subsequently use this information in group-based models in
order to improve individuals' predictive performance. More specifically, two
model-based clustering approaches are examined, where the first is using
model-extracted parameters of personalized models, whereas the second is
optimized on the model-based forecasting performance. Both methods are then
analyzed using intrinsic clustering evaluation measures (e.g. Silhouette
coefficients) as well as the performance of a downstream forecasting scheme,
where each forecasting group-model is devoted to describe all individuals
belonging to one cluster. Among these, clustering based on performance shows
the best results, in terms of all examined evaluation measures. As another
level of evaluation, those group-models' performance is compared to three
baseline scenarios, the personalized, the all-in-one group and the random
group-based concept. According to this comparison, the superiority of
clustering-based methods is again confirmed, indicating that the utilization of
group-based information could be effectively enhance the overall performance of
all individuals' data.
