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A technique for providing biobehavioral rhythm models that generate a series of characteristic features which are further used for measuring stability in biobehavioral rhythms and to predict different outcomes such as health status through a machine learning component. A computational framework is provided for modeling biobehavioral rhythms from mobile and wearable data streams that rigorously processes sensor streams, detects periodicity in data, models rhythms from that data and uses the cyclic model parameters to predict an outcome. The framework can reliably discover various peri ods of different length in data, extract cyclic biobehavioral characteristics through exhaustive modeling of rhythms for each sensor feature; and provide the ability to use different combination of sensors and data features to predict an outcome.