Human behavior is often complex and context-dependent. This paper presents a general technique to exploit this ``multidimensional'' contextual variable for human mobility prediction. We use an ensemble method, in which we extract different mobility patterns with multiple models and then combine these models under a probabilistic framework. The key idea lies in the assumption that human mobility can be explained by several mobility patterns that depend on a subset of the contextual variables and these can be learned by a simple model. We showed how this idea can be applied to two specific online prediction tasks: \textit{what is the next place a user will visit?} and \textit{how long will he stay in the current place?}. Using smartphone data collected from 153 users during 17 months, we show the potential of our method in predicting human mobility in real life.