A Variational Bayesian Deep Network with Data Self‐Screening Layer for Massive Time‐Series Data Forecasting

Abstract

Compared with mechanism‐based modeling methods, data‐driven modeling based on big data has become a popular research field in recent years because of its applicability. However, it is not always better to have more data when building a forecasting model in practical areas. Due to the noise and conflict, redundancy, and inconsistency of big time‐series data, the forecasting accuracy may reduce on the contrary. This paper proposes a deep network by selecting and understand-ing data to improve performance. Firstly, a data self‐screening layer (DSSL) with a maximal information distance coefficient (MIDC) is designed to filter input data with high correlation and low redundancy; then, a variational Bayesian gated recurrent unit (VBGRU) is used to improve the anti-noise ability and robustness of the model. Beijing’s air quality and meteorological data are con-ducted in a verification experiment of 24 h PM2.5 concentration forecasting, proving that the proposed model is superior to other models in accuracy.