Empirical mode decomposition using deep learning model for financial market forecasting

ABSTRACT: Financial market forecasting is an essential component of financial systems; however, predicting financial market trends is a challenging job due to noisy and non-stationary information. Deep learning is renowned for bringing out excellent abstract features from the huge volume of raw data without depending on prior knowledge, which is potentially fascinating in forecasting financial transactions. This article aims to propose a deep learning model that autonomously mines the statistical rules of data and guides the financial market transactions based on empirical mode decomposition (EMD) with back-propagation neural networks (BPNN). Through the characteristic time scale of data, the intrinsic wave pattern was obtained and then decomposed. Financial market transaction data were analyzed, op

SUBMITTER: Jin Z 

PROVIDER: S-EPMC9575866 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature