  Accurate reporting and forecasting of PM2.5 concentration are important for
improving public health. In this paper, we propose a daily prediction method of
PM2.5 concentration by using data-driven ordinary differential equation (ODE)
models. Specifically, based on the historical PM2.5 concentration, this method
combines genetic programming and orthogonal least square method to evolve the
ODE models, which describe the transport of PM2.5 and then uses the data-driven
ODEs to predict the air quality in the future. Experiment results show that the
ODE models obtain similar prediction results as the typical statistical model,
and the prediction results from this method are relatively good. To our
knowledge, this is the first attempt to evolve data-driven ODE models to study
PM2.5 prediction.
