Predictive Analytics Models for Commodity Market: A Literature Review
Keywords:
Artificial Intelligence, Machine Learning, Predictive Analytics, ForecastingAbstract
This research presents a literature review on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in predictive analytics, with a main emphasis on the commodity market. Predictive analytics influences statistical models and Machine Learning approaches to analyze periodic data and predict future outcomes, providing substantial value across various domains such as finance, healthcare, retail, marketing, and commodities. The motive of this review is two-fold: (1) to survey AI-ML based approaches, techniques, and tools employed in predictive analytics, and (2) to inspect predictive analytics models precisely designed for the commodity market. The review integrates current methodologies, strategies, algorithms, and tools, while also surveying the types of commodities analyzed and their interdependencies. The major influence of this research lies in recognizing major gaps in existing studies, predominantly in model generalization, integration of heterogeneous data sources, and evaluation of real-world applicability. These gaps emphasize possibilities for future research and provide direction for developing more robust, accurate, and scalable predictive analytics frameworks.
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