A DeepFM-Based Non-Parametric Model Enabled Big Data Platform for Predicting Passenger Car Sales in Sustainable Way

A rational approach to predict passenger car sales and analyze the current state of the passenger car sales market can contribute to the healthy development of the automotive industry. The number of features used to describe passenger car sales in real life is too redundant. The features of human empirical filtering and combination cause loss of time. In addition, explicit features are too homogeneous, which need to be complemented by implicit features. The fixed-parameter weights in the trained model cannot provide some estimates for unknown uncertainties. Therefore, in this work, we propose a DeepFM-based nonparametric model (DFMNP) for predicting passenger car sales in sustainable way. We use a big data platform to provide data for the DFMNP model. The DFMNP model uses feature engineering to expand the number of explicit features, a multilayer neural network to extract implicit features, and a Bayesian neural network to replace the neural network with fixed weights for inferring predictive values. In addition, a factorization machine is used in the prediction function to take into account the cross information of implicit features. The combination of the above improvement points can be used to improve the model’s expressive and predictive power for unknown data. Its prediction performance on two real passenger car sales datasets is as follows, RMSE values of 0.0825 and 0.116, and MAE values of 0.0482 and 0.0595. The above experimental results verify the superiority of the method proposed in this work.