Depression has become a social problem in Japan. To prevent depression, people need to recognize their mental health in daily life. Previous research supports mental health care by predicting tomorrow's mood with 73% accuracy using weather information and biological information. However, the mood after 2 d or later could not be predicted using an existing system. In this paper, we propose multilayer-deep neural network (M-DNN) and storage-type prediction models (STPMs) to predict mood two weeks in advance with high accuracy. The M-DNN outputs predictions as well as unpredictable data using a deep neural network and threshold optimization in each prediction layer. The threshold optimization determines the threshold that maximizes a certainty factor. The certainty factor is calculated from the predictive accuracy of M-DNN and the amount of unpredictable data. The STPMs interpolates the unpredictable data by accumulating the predictions output from M-DNN. The amount of unpredictable data output from the M-DNN is decreased by STPMs. Experiments show that M-DNN and STPMs can predict mood two weeks in advance with 70% accuracy. The predictive accuracy in M-DNN+STPMs is 11% higher than that in DNN. Hence, M-DNN+STPMs is an effective method for mood prediction.