Forecasting hourly PM_2.5 based on deep temporal convolutional neural network and decomposition method

For hourly PM_2.5 concentration prediction, accurately capturing the data patterns of external factors that affect PM_2.5 concentration changes, and constructing a forecasting model is one of efficient means to improve forecasting accuracy. In this study, a novel hybrid forecasting model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and deep temporal convolutional neural network (DeepTCN) is developed to predict PM_2.5 concentration, by modeling the data patterns of historical pollutant concentrations data, meteorological data, and discrete time variables’ data. Taking PM_2.5 concentration of Beijing as the sample, experimental results showed that the forecasting accuracy of the proposed CEEMDAN-DeepTCN model is verified to be the highest when compared with the statistics-based models, traditional machine learning models, the popular deep learning models and several existing hybrid models. The new model has improved the capability to model the PM_2.5-related factor data patterns, and can be used as a promising tool for forecasting PM_2.5 concentrations.