Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation management

Abstract

Introduction: This paper proposes a deep learning algorithm based on the VMD-SSA-BiLSTM model for time series forecasting in the smart grid financial market. The algorithm aims to extract useful information from power grid signals to improve the timing prediction accuracy and meet the needs of sustainable innovation management.Methods: The proposed algorithm employs the variational mode decomposition (VMD) method to decompose and reduce the dimensionality of historical data, followed by singular spectrum analysis (SSA) to perform singular spectrum analysis on each intrinsic mode function component. The resulting singular value spectrum matrices serve as input to a bidirectional long short-term memory (BiLSTM) neural network, which learns the feature representation and prediction model of the smart grid financial market through forward propagation and backpropagation.Results: The experimental results demonstrate that the proposed algorithm effectively predicts the smart grid financial market's time series, achieving high prediction accuracy and stability. The approach can contribute to sustainable innovation management and the development of the smart grid.Discussion: The VMD-SSA-BiLSTM algorithm's efficiency in extracting useful information from power grid signals and avoiding overfitting can improve the accuracy of timing predictions in the smart grid financial market. The algorithm's broad application prospects can promote sustainable innovation management and contribute to the development of the smart grid.