Deep prediction on financial market sequence for enhancing economic policies

Abstract

Numerous sectors are significantly impacted by the quick advancement of image and video processing technologies. Investors can kind knowledgeable savings choices based on the examination and projection of financial bazaar income, and the government can create accurate policies for various forms of economic control. This study uses an artificial rabbits optimization algorithm in image processing technology to examine and forecast the returns on financial markets and multiple indexes using a deep-learning LSTM network. This research uses the time series technique to record the regional correlation properties of financial market data. Convolution pooling in LSTM is then used to gather significant details concealed in the time sequence information, generate the data's tendency bend, and incorporate the structures using technology for image processing to ultimately arrive at the forecast of the economic sector's moment series earnings index. A popular artificial neural network used in time series examination is the long short-term memory (LSTM) network. It can accurately forecast financial marketplace values by processing information with numerous input and output timesteps. The correctness of financial market predictions can be increased by optimizing the hyperparameters of an LSTM model using metaheuristic procedures like the Artificial Rabbits Optimization Algorithm (ARO). This research presents the development of an enhanced deep LSTM network with the ARO method (LSTM-ARO) for stock price prediction. According to the findings, the research's deep learning system for financial market series prediction is efficient and precise. Data analysis and image processing technologies offer practical approaches and significantly advance finance studies.