AQI time series prediction based on a hybrid data decomposition and echo state networks.

Abstract

A hybrid AQI time series prediction model is proposed based on EWT-SE-VMD secondary decomposition, ICA (imperialist competitive algorithm) feature selection, and ESN (echo state network) neural network. Firstly, EWT (empirical wavelet transform) and VMD (variational mode decomposition) are used to decompose the original AQI time series into several stable and reliable subseries. Then, the ICA is used to select features of the above subseries for the ESN prediction model. Finally, the optimized feature variables are put into the ESN deep network to establish a prediction model of each AQI subseries and obtain the future AQI index. According to the experimental results of the daily AQI series in Beijing, Tianjin, and Shijiazhuang, we find that (a) among all decomposition methods, the proposed secondary decomposition method (EWT-SE-VMD) performs best in processing data; (b) it is proved that the proposed hybrid model has broad application prospect and research value in the AQI prediction field.