MATLAB Code Implementation for Stock Market Prediction
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The MATLAB program for stock market prediction serves as a robust tool that enables users to select from various neural network types, offering enhanced flexibility and scalability. The implementation typically includes configuration parameters for network architectures (such as feedforward, LSTM, or GRU networks), data preprocessing modules for financial time-series data, and customizable training algorithms. This framework can be extended to other domains like finance, economics, and market analysis through modular code design, where key functions handle feature extraction, normalization, and performance validation. The object-oriented structure allows for easy integration of additional prediction models and datasets, providing users with broad application possibilities.
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