The methods commonly used for the time series prediction of the smart grid financial market mainly include time series models, regression models, integrated models, and deep learning models. According to the work of Górski (2018), the most important factor is that the model can process time series data in real time and make predictions quickly so that it can support the time series of the real-time smart grid financial market prediction and sustainable development of smart grid through sustainable innovation management. Modulo accuracy: Our model can also deal with the relationship between multiple variables involved in the financial order of the smart grid to predict the interaction between various indicators better, and it can use the decomposition and feature extraction of time series data to improve the interpretability of the model to better understand the changing rules of the data and the forecast results. Therefore, this paper proposes a smart grid financial order algorithm based on the VMD–SSA–BiLSTM model. There still needs to be more in-depth research and exploration on applying the VMD–-SSA–BiLSTM model in smart grid financial time series forecasting and sustainable innovation management. However, the model does not consider problems such as nonlinearity and non-stationarity of the time series. In addition, some scholars proposed a forecasting model based on deep learning and particle swarm optimization, which can forecast the electricity market at different time scales. Still, the model does not consider the intrinsic structure and characteristics of the time series. For example, some scholars proposed a forecasting model based on multi-scale chaotic Fourier transform and long short-term memory (LSTM), which can forecast the electricity market at multiple time scales. In recent studies, many scholars have also focused on the forecasting problem of smart grid financial markets and proposed some new forecasting models. As a deep learning algorithm, the VMD–SSA–BiLSTM model has the advantages of adaptability, high precision, and high stability and has achieved good results in time series data analysis and forecasting. In recent years, the rapid development of deep learning technology has provided a more convenient and efficient application solution for timing forecasting and sustainable innovation management of smart grid financial markets ( Frisch, 2019). Its main purpose is to use artificial intelligence and data analysis technology to predict the timing changes of the smart grid financial market and to promote the sustainable development of the smart grid through sustainable innovation management. Time series forecasting and sustainable innovation management of the smart grid financial market are comprehensive subjects involving energy, finance, and sustainable development. The algorithm's broad application prospects can promote sustainable innovation management and contribute to the development of the smart grid. The approach can contribute to sustainable innovation management and the development of the smart grid.ĭiscussion: 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. 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 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. 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 algorithm aims to extract useful information from power grid signals to improve the timing prediction accuracy and meet the needs of sustainable innovation management. 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.
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