In spite of the significant work that has been done todiscover and recognize activities in the smart home re-search, less attention has been paid to predict the futureactivities that the resident is likely to perform. An ac-tivity prediction module can play a major role in designof a smart home. For instance, by taking advantage ofan activity prediction module, a smart home can learncontext-aware rules to prompt individuals to initiate im-portant activities. In this paper, we propose an activityprediction approach using Bayesian networks. We pro-pose a novel two-step inference process to predict thenext activity features and then to predict the next activ-ity label. We also propose an approach to predict thestart time of the next activity which is based on model-ing the relative start time of the predicted activity usinga continuous normal distribution and outlier detection.We evaluate our proposed models using real data col-lected from two smart home apartments.