  With the increase of the number of elderly people living alone around the
world, there is a growing demand for sensor-based detection of anomalous
behaviors. Although smart homes with ambient sensors could be useful for
detecting such anomalies, there is a problem of lack of sufficient real data
for developing detection algorithms. For coping with this problem, several
sensor data simulators have been proposed, but they have not been able to model
appropriately the long-term transitions and correlations between anomalies that
exist in reality. In this paper, therefore, we propose a novel sensor data
simulator that can model these factors in generation of sensor data. Anomalies
considered in this study were classified into three types of \textit{state
anomalies}, \textit{activity anomalies}, and \textit{moving anomalies}. The
simulator produces 10 years data in 100 min. including six anomalies, two for
each type. Numerical evaluations show that this simulator is superior to the
past simulators in the sense that it simulates well day-to-day variations of
real data.
