Hybridizing machine learning with time series analysis for enhanced forecasting in management science and operational efficiency: a systematic review

In the dynamic landscape of management science, this systematic review provides a comprehensive exploration of the amalgamation of machine learning techniques with traditional time series analysis methods. As time series analysis continues to play an increasingly pivotal role in enhancing managerial decision-making processes by offering insights derived from sequential data points, this study endeavors to shed light on the multifaceted applications and synergistic benefits resulting from the integration of time series analysis with machine learning. By scrutinizing a diverse array of studies and practical implementations, the study aims to illuminate the rich potential of this hybrid approach across various domains, including market trend forecasting, inventory management, financial management, and operational efficiency. Through an in-depth analysis, this review elucidates how the fusion of machine learning and time series analysis contributes to heightened forecasting accuracy and operational efficacy, thus empowering decision-makers with more robust insights and strategies.