For executing flexible support or substitution for residents’ behaviors, it is important to recognize and predict their ever-changing activities. From that background, we realized some novel methods of sensing residents’ data of daily life and predicting the target behaviors for support, such as eating, sleeping, etc. Supposing that each behavior in a living space is with some kinds of staying at the corresponding location, the recognizing method grasps the potential chain of the resident’s activities by segmenting one’s accumulated trajectories into staying or moving and by classifying the staying. And then, the method predicts the start time of the target behaviors from their preceding behaviors, mining time series association rules of transition events of segmented trajectories. The experimental results using real residents’ trajectory data in a existing house of almost two years demonstrate that the behaviors which have movement as preparation of them can be predicted with substantial precision and show the possibility of the behavior prediction in a living space.