Big Data-Based Predictive Models for Intelligent Forecasting

Péter Szabó

Big Data-Based Predictive Models for Intelligent Forecasting

Author(s)	Prof. Péter Szabó
Country	Hungary
Abstract	The rapid expansion of digital technologies has resulted in the generation of massive volumes of structured, semi-structured, and unstructured data across organizational, commercial, scientific, and social environments. The increasing availability of such data has created significant opportunities for developing predictive models capable of forecasting future events, behaviors, demands, risks, and operational conditions. Conventional forecasting techniques often depend on limited datasets, predefined assumptions, and relatively stable relationships among variables, whereas Big Data-based predictive models can integrate diverse and continuously generated information to improve forecasting capabilities. This study examines the role of Big Data-based predictive models in intelligent forecasting and develops a conceptual framework connecting large-scale data acquisition, preprocessing, feature engineering, predictive modeling, forecast generation, and performance evaluation. A conceptual and descriptive research methodology is employed through a structured synthesis of scholarly literature addressing Big Data analytics, predictive analytics, machine learning, artificial intelligence, forecasting, distributed computing, and data-driven decision-making. The study identifies data quality, feature relevance, computational scalability, model accuracy, adaptability, processing speed, and interpretability as important determinants of forecasting effectiveness. The proposed framework integrates descriptive, predictive, and intelligent analytical capabilities to transform large-scale datasets into future-oriented insights. Machine-learning approaches can identify nonlinear relationships and complex patterns that may not be captured by traditional forecasting techniques. Big Data can further improve forecasting by combining information from multiple sources, including transactional databases, sensor networks, customer interactions, market indicators, social media, and historical records. The analysis indicates that predictive models can support demand forecasting, financial risk assessment, supply-chain planning, customer behavior prediction, healthcare planning, energy management, and operational optimization. The study concludes that Big Data-based predictive models provide a strong foundation for intelligent forecasting when appropriate data-management strategies, scalable computational infrastructure, adaptive algorithms, and rigorous evaluation procedures are integrated.
Keywords	Big Data, Predictive Models, Intelligent Forecasting, Predictive Analytics, Machine Learning, Artificial Intelligence, Data Analytics, Forecasting Systems, Data-Driven Decision-Making, Big Data Analytics
Field	Engineering
Published In	Volume 2, Issue 2, March-April 2020
Published On	2020-04-22

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Big Data-Based Predictive Models for Intelligent Forecasting

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