Novel delayed binary time-series pattern based machine learning techniques for stock market forecasting

This study proposes an innovative machine learning technique for stock market forecasting that leverages delayed binary time-series patterns to enhance prediction accuracy. By employing an XNOR operation in conjunction with a structured analysis of historical stock price data, this approach effectively identifies underlying patterns and dependencies across multiple time windows. The research systematically validates its methodology against several established machine learning classifiers, including Random Forest (RF), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) networks. Notably, the findings indicate that the Decision Tree model, despite a slight reduction in accuracy compared to LSTM, exhibits superior overall performance in trend forecasting. The results suggest a paradigm shift in stock market prediction practices, highlighting the potential of integrating delayed time-series analysis with existing techniques to achieve improved robust outcomes. This work lays the groundwork for further exploration into diverse datasets and adaptive modeling strategies in financial forecasting.