Financial Time Series Forecasting: A Combinatorial Forecasting Model Based on STOA Optimizing VMD

Abstract

The complexity and frequent fluctuations of economic data pose a significant challenge to forecasting studies. In order to predict financial data more accurately, we build a fusion model for predicting financial data based on the idea of decomposition-recombination by combining the Sooty Tern Optimization Algorithm (STOA), Variational Mode Decomposition (VMD), Support Vector Machine (SVM) and Back Propagation Neural Network (BPNN). Firstly, after finding the optimal parameters of the VMD by the STOA, adjust the parameters of the VMD and decompose the financial data, remove the residual from the series, and constitute the remaining necessary information within the financial data into new series modeled by applying the SVM. Finally, calculate the error between the predicted and actual values. Notably, the residual is treated specially in the modeling. A Double-layer BPNN model is used to establish a mechanism to increase the sensitivity of the model fluctuations. The influence factor series and the residual are introduced as input variables to the BPNN to establish a mapping relationship between them and the error series. The results show that the model improves data utilization through five experiments, solves the problem of the VMD insensitivity to fluctuations, and improves the prediction accuracy of financial time series effectively.