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Through Ecological Momentary Assessment (EMA) studies, a number oftime-series data is collected across multiple individuals, continuouslymonitoring various items of emotional behavior. Such complex data is commonlyanalyzed in an individual level, using personalized models. However, it isbelieved that additional information of similar individuals is likely toenhance these models leading to better individuals' description. Thus,clustering is investigated with an aim to group together the most similarindividuals, and subsequently use this information in group-based models inorder to improve individuals' predictive performance. More specifically, twomodel-based clustering approaches are examined, where the first is usingmodel-extracted parameters of personalized models, whereas the second isoptimized on the model-based forecasting performance. Both methods are thenanalyzed using intrinsic clustering evaluation measures (e.g. Silhouettecoefficients) as well as the performance of a downstream forecasting scheme,where each forecasting group-model is devoted to describe all individualsbelonging to one cluster. Among these, clustering based on performance showsthe best results, in terms of all examined evaluation measures. As anotherlevel of evaluation, those group-models' performance is compared to threebaseline scenarios, the personalized, the all-in-one group and the randomgroup-based concept. According to this comparison, the superiority ofclustering-based methods is again confirmed, indicating that the utilization ofgroup-based information could be effectively enhance the overall performance ofall individuals' data.

Model-based Clustering of Individuals' Ecological Momentary Assessment Time-series Data for Improving Forecasting Performance