LAUSR

Abstract Financial data forecasting is conducive to get a better understanding of the future economic situation. Recently, variational mode decomposition (VMD) is introduced into the field of financial data forecasting. However, the prediction accuracy of the current methods is low. We propose a new VMD-based financial data prediction model. In this model, the genetic algorithm is utilized to optimize the parameters of VMD. Then VMD decomposes the data sequence into long-term and short-term trends. Finally, we employ the long short-term memory (LSTM) network to predict the future data with inputs generated by VMD. The contributions are: (1) We propose an improved VMD and LSTM based financial data forecasting model; (2) A guideline on the parameter selection of VMD to process financial data is designed; (3) A prediction-error reducing method which reduces the inherent error caused by VMD insensitivity to fluctuation is proposed. Experimental results indicate our model is accuracy-promising and superior to the baseline models in one-step-ahead forecasting of financial time series.