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It is well known that people movement exhibits a high degree of repetition since people visit regular places and make regular contacts for their daily activities. This paper presents a novel framework named Jyotish, which constructs a predictive model by exploiting the regularity of people movement found in the real joint Wifi/Bluetooth trace. The constructed model is able to answer three fundamental questions: (1) where the person will stay, (2) how long she will stay at the location, and (3) who she will meet. In order to construct the predictive model, Jyotish includes an efficient clustering algorithm to cluster Wifi access point information in the Wifi trace into locations. Then, we construct a Naive Bayesian classifier to assign these locations to records in the Bluetooth trace and obtain a fine granularity of people movement. Next, the fine grain movement trace is used to construct the predictive model including location predictor, stay duration predictor, and contact predictor to provide answers for three questions above. Finally, we evaluate the constructed predictive model over the real Wifi/Bluetooth trace collected by 50 participants in University of Illinois campus from March to August 2010. Evaluation results show that Jyotish successfully constructs a predictive model, which provides a considerably high prediction accuracy of people movement.