The Indian stock market is a dynamic, complicated system that is impacted by many different variables, making it difficult to anticipate its future. The utilization of deep learning and optimization techniques to forecast stock market movements has gained popularity in recent years. To foresee theIndian stock market, an innovative approach is presented in this study that combines the Grey Wolf Optimization algorithm with a hybrid Convolutional Neural Network (CNN) and Bi-Directional Long-Short Term Memory (Bi-LSTM) framework. The stock market information is first pre-processed utilizing a CNN to extract pertinent features. The Bi-LSTM system, that is intended to capture the long-term dependencies and temporal correlations of the stock market statistics, is then fed the CNN’s outcome. The model parameters are then optimized utilizing the Grey Wolf Optimization (GWO) technique, which also increases forecasting accuracy. The findings demonstrate that, in terms of forecasting accuracy, the suggested method outperforms a number of contemporary methods, including conventional time series models, neural networks, and evolutionary algorithms. Thus, the suggested methodology provides an effective way to forecast the Indian stock market by combining deep learning and optimization approaches.

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使用混合卷积神经网络和双向长短期记忆模型的灰狼优化来预测股市

印度股市是一个动态、复杂的系统，受到许多不同变量的影响，因此很难预测其未来。近年来，利用深度学习和优化技术来预测股市走势越来越受欢迎。为了预测印度股市，本研究提出了一种创新方法，将灰狼优化算法与混合卷积神经网络 (CNN) 和双向长短期记忆 (Bi-LSTM) 框架相结合。首先利用 CNN 对股票市场信息进行预处理，以提取相关特征。Bi-LSTM 系统旨在捕获股票市场统计数据的长期依赖性和时间相关性，然后将其输入 CNN 的结果。然后利用灰狼优化 (GWO) 技术优化模型参数，这也提高了预测准确性。研究结果表明，就预测准确性而言，所提出的方法优于许多当代方法，包括传统的时间序列模型、神经网络和进化算法。因此，所建议的方法通过结合深度学习和优化方法，提供了一种预测印度股市的有效方法。