  Predicting smartphone users activity using WiFi fingerprints has been a
popular approach for indoor positioning in recent years. However, such a high
dimensional time-series prediction problem can be very tricky to solve. To
address this issue, we propose a novel deep learning model, the convolutional
mixture density recurrent neural network (CMDRNN), which combines the strengths
of convolutional neural networks, recurrent neural networks and mixture density
networks. In our model, the CNN sub-model is employed to detect the feature of
the high dimensional input, the RNN sub-model is utilized to capture the time
dependency and the MDN sub-model is for predicting the final output. For
validation, we conduct the experiments on the real-world dataset and the
obtained results illustrate the effectiveness of our method.
