| Abstract: |
| Numerous sectors are greatly impacted by the quick advancement of image and video processing
technologies. Investors can make informed investment decisions based on the analysis and projection of
ffnancial market income, and the government can create accurate policies for various forms of economic
control. This study uses an artiffcial rabbits optimization algorithm in image processing technology to
examine and forecast the returns on ffnancial markets and various indexes using a deep learning LSTM
network. To successfully record the regional correlation properties of ffnancial market data, this research
uses the time series technique. Convolution pooling in LSTM is then used to gather signiffcant details hidden
in the time series data, generate the data`s trend curve, and incorporate the features using technology
for image processing to ultimately arrive at the prediction of the ffnancial sector`s moment series earnings
index. A popular kind of artiffcial neural network used in time series analysis is the Long Short-Term
Memory (LSTM) network. By processing data with numerous input and output timesteps, it can accurately
forecast ffnancial market prices. The correctness of ffnancial market predictions can be increased
by optimizing the hyperparameters of an LSTM model using metaheuristic algorithms like the Artiffcial
Rabbits Optimization Algorithm (ARO). This research presents the development of an optimized
deep LSTM network with the ARO model (LSTM-ARO) for stock price prediction. The research`s deep
learning system for ffnancial market series prediction is efffcient and precise, according to the ffndings.
Technologies for data analysis and image processing offer useful approaches and signiffcantly advance
the study of ffnance. |
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