Development of a Robust Stock Market Prediction Mechanism based on Enhanced Comprehensive Learning Principles

The use of deep learning to forecast stock market prices as well as trends has become increasingly common in the age of big data. Among the most complex challenges in machine learning is the prediction of share values, a task that is intricately tied to a multitude of influencing factors affecting supply and demand dynamics. This study introduces innovative hybrid methodologies for forecasting both stock market indices and stock prices. The initial approach involves the utilization of a Long Short-Term Memory (LSTM) Network. To enhance its predictive capabilities, the training process employs the backpropagation algorithm. Subsequently, the study incorporates an Autoregressive Integrated Moving Average (ARIMA) Model. This model not only unveils fundamental insights into prediction trends but also provides visual representations of projected outcomes. With an average prediction accuracy of 97.66%, the ARIMA model yields satisfactory results. However, it's worth noting that achieving this high accuracy demands the utilization of substantial training data and a considerable number of training epochs. To amplify predictive performance, the entire dataset is systematically divided into 15 distinct segments. Each segment is individually fed into the LSTM model, resulting in the attainment of maximum accuracy for each respective segment. This segmentation strategy has a profound impact on both the model's training time and prediction accuracy, underscoring the critical role of input volume in model performance.