Enterprise financial management and trend prediction based on time series analytics and edge computing

Abstract

In order to improve the prediction accuracy and reliability of long‐term financial trend, a financial time series prediction framework is proposed by combining empirical mode decomposition (EMD) and reduced support vector regression (SVR). Through the empirical mode decomposition, the framework can eliminate the disturbance caused by the multi band information of error sequence. The financial time series processed by EMD are used to train a reduced support vector regression model. Compared with classical support vector regression, the reduced support vector regression can discarded the samples, which would not become support vectors, to reduce the scale of problem. Therefore, the reduced support vector regression is much faster than support vector regression and is more suitable for edge computing. The experiments on benchmark dataset show that empirical mode decomposition plus reduced support vector regression can reach the close performance of empirical mode decomposition plus support vector regression, meanwhile running time only costs less than one fortieth of empirical mode decomposition plus support vector regression's.