Big Data Application in Forecasting Financial Investment of e-Commerce Industry for Sustainability

Abstract

With the rapid development of e-commerce, financial investment forecasting in the e-commerce industry has gradually become a concern of relevant personnel. Based on DBN, the study proposes a PVD prediction model. For training and test sample sets, the PLR_VIP algorithm is calculated and min-max normalization is applied to the original financial time series. To determine appropriate network parameters, the DBN network is trained and tested, and then Elliott wave patterns are predicted based on financial time series. The experimental results show that the MSE of the PVD model is 0.4015 and the prediction accuracy is 70.21%, indicating that it can efficiently and accurately identify the Elliott wave pattern of financial time series. Comparing the prediction results of the PVD model with the other five models, the values of the four evaluation indicators of PVD are the lowest among all models, which are 0.6336, 0.4015, 0.9052, and 29.79%, respectively. Compared with the training error changes of other models, it can be seen that the error curve of the DBN network is smoother and the training error is smaller. It shows that it has higher stability, faster convergence speed, higher reliability and accuracy, and shows excellent prediction performance, which is significantly better than other models. Experiments show that under the background of sustainable development, the PVD forecasting model proposed in the study performs well in financial investment forecasting, which provides a reference for the development of financial investment forecasting in the e-commerce industry.