Sensors are active everywhere. Enormous volumes of sensed events are sent over the data streams, while most of applications want to focus on events that would be curious. We propose a technique for mining periodicities and predicting its outliers from the stream. The key to our technique is a simple periodic pattern {\Delta x}t, derived from delta-time mining, or SUP(t, t+{\Delta x}t). We provide efficient algorithms for finding the highest support {\Delta x}t on a small and resource-limited sensor device. Our experiments will compare memory efficiency and accuracy, on a variety of event patterns, monogenesis, polygenesis, and semi-random.