Linear Algebraic Approach for Delayed Patternized Time-Series Forecasting Models

This paper introduces a linear algebraic approach for forecasting time-series trends, leveraging a theoretical model that transforms historical stock data into matrices to capture temporal dynamics and market patterns. By employing an analytical approach, the model predicts future market movements through delayed patternized time-series machine learning training, achieving an impressive accuracy of 83.77% across 10,539 stock data samples. The mathematical proof underlying the framework, including the use of validation matrices and NXOR operations, ensures a structured evaluation of predictive accuracy. The binary trend-based simplification further reduces computational complexity, making the model scalable for large datasets. This study highlights the potential of linear algebra in enhancing predictive models and provides a foundation for future research to refine the framework, incorporate external variables, and explore alternative machine learning algorithms for improved robustness and applicability in financial markets. The primary advantages of employing linear algebra in this research lay in its ability to systematically structure high-dimensional financial data, enhance computational efficiency, and enable rigorous validation. The results indicate not only the efficacy in trend forecasting but also its potential applicability across various financial settings, making it a valuable tool for investors seeking data-driven insights into market trends. This research paves the way for future studies aimed at refining forecasting methodologies and enhancing financial decision-making processes.