Machine Learning-Based Forecasting Models for Dynamic System Management

Loo Chu Kiong

Machine Learning-Based Forecasting Models for Dynamic System Management

Author(s)	Dr. Loo Chu Kiong
Country	Malaysia
Abstract	The increasing complexity of modern digital and operational environments has created a strong requirement for intelligent forecasting systems capable of supporting dynamic system management. Conventional forecasting approaches frequently depend on static assumptions, historical averages, or predefined statistical relationships, which may become inadequate when system conditions change rapidly. Machine learning provides advanced forecasting capabilities by identifying complex patterns and relationships within historical and real-time data. This study examines machine learning-based forecasting models for dynamic system management and proposes an integrated conceptual framework for predictive and adaptive decision-making. The study adopts a conceptual and descriptive research design based on secondary literature concerning machine learning, time-series forecasting, predictive analytics, intelligent systems, dynamic resource management, and automated decision support. The proposed framework integrates data acquisition, preprocessing, feature engineering, forecasting-model development, prediction validation, decision generation, and continuous feedback. The analysis indicates that machine learning forecasting can support improved demand prediction, resource allocation, workload management, anomaly anticipation, and operational planning. However, challenges involving data quality, model drift, computational complexity, forecasting uncertainty, interpretability, and changing system environments remain significant. The study concludes that machine learning-based forecasting provides an important foundation for intelligent dynamic system management and can support more responsive, efficient, and adaptive decision-making across multidisciplinary technological environments.
Keywords	Machine Learning, Forecasting Models, Dynamic System Management, Predictive Analytics, Time-Series Forecasting, Intelligent Systems, Demand Prediction, Adaptive Management.
Field	Engineering
Published In	Volume 1, Issue 2, March-April 2019
Published On	2019-03-31

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American International Journal of Advanced Research and Development

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Machine Learning-Based Forecasting Models for Dynamic System Management

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