RETRACTED: Application of VMD–SSA–BiLSTM algorithm to smart grid financial market time series forecasting and sustainable innovation management - Yin, Chengran; Wang, Guangming; Liao, Jiacheng (Frontiers in Energy Research, 2023)
The topic of time series forecasting and sustainable innovation management of smart grid financial market is an important research field, which involves knowledge of multiple disciplines and fields and can contribute to the development and sustainable development of the smart grid. This paper proposes a deep learning algorithm based on the VMD-SSA-BiLSTM model. This algorithm can extract useful information from power grid signals more efficiently, avoid overfitting, and improve the timing prediction accuracy of smart grid financial markets-continuous innovation management. The model first uses the Variational Mode Decomposition (VMD) method to decompose and reduce the dimensionality of the historical data of the smart grid financial market to obtain multiple Intrinsic mode function (IMF) components; then uses the Singular Spectrum Analysis (SSA) method to perform singular spectrum analysis on each IMF component to obtain the corresponding singular value spectrum matrix; finally, All the singular value spectrum matrices are used as the input of the Bidirectional Long Short-term Memory Neural Network (BiLSTM) network, and the feature representation and prediction model of the smart grid financial market are learned through the forward propagation and backpropagation process of the network.Through the experimental results, we can find that the algorithm can be effectively applied to the time series prediction of the smart grid financial market to meet the needs of sustainable innovation management and has high prediction accuracy and stability. Therefore, our algorithm has broad application prospects in the timing forecast of the smart grid financial market and sustainable innovation management and can contribute to the development and sustainable development of the smart grid.