A Hybrid Optimization Deep Learning Frame Work for Efficient Stock Market Forecasting

The widespread need to forecast future market trends has prompted the incorporation of cutting-edge technology that go beyond the realm of conventional statistical models. Predicting the future of the stock market using ML and DL methods is the focus of this article. We offer a holistic strategy that incorporates effective methods for feature selection, data preparation, and classification. To clean up the data and lower the noise level, the wavelet transform technique is used. By determining which input features are most important, the Dandelion-Optimization-Algorithm(DOA) optimizes feature selection. An innovative model for analyzing stock market data is created, 3DCNN-GRU, which combines a 3DCNNwith a gated-recurrent-unit(GRU). The Blood-Coagulation-Algorithm(BCA) makes it easier to tune hyperparameters, which improves the performance of the model. The outstanding prediction accuracy of 99.14% achieved by our methodology demonstrates its durability and efficacy in stock market anticipating applications. The proposed model has a lot of potential, but it can only handle the Nifty 50 index because that's all the dataset covers. The work's broader implications imply that the model might be further validated and its applicability improved by including additional datasets and investigating alternative market conditions. To guarantee robustness and generalizability, future research could concentrate on using this model in different financial circumstances.