Dynamic pre-training of Deep Recurrent Neural Networks for predicting environmental monitoring data

In this paper, we introduce a Deep Recurrent Neural Network (DRNN) that is trained using a novel autoencoder pre-training method especially designed for the task of time series prediction. Our main objective is to perform predictions of environmental monitoring data using open sensors with improved accuracy over the currently employed methods. The numerical experiments show that our proposed pre-training method is superior that a canonical and a state-of-the-art auto-encoder training method when applied to time series prediction. On the specific case of fine particulate matter (PM_2.5) forecasting in Japan, the experiments confirm that when compared against the PM_2.5 prediction system VENUS employed by the Japanese Government, our technique improves the accuracy of PM_2.5 concentration level predictions that are being reported in Japan.