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2 articles for “Commodity price forecasting”
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Forecasting Commodity Prices Using Deep Learning Techniques: An Empirical Evidence from India
Abstract: Commodity price forecasting is instrumental in financial markets, providing framework for investment choices and risk management practices. Traditional models, including statistical and machine learning approaches, have limitations in capturing the nonlinear and volatile nature of commodity prices. Deep learning (DL) techniques have emerged as promising alternatives, leveraging advanced neural networks to enhance predictive accuracy. This study presents a thorough and comprehensive examination of deep learning applications in commodity price prediction, …
Published in Journal of Computer Technology & Applications · Vol. 16, Issue 3, 2025 · pp. 08–12 Read article
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Demand Forecasting for Perishable Food Commodities Using Data Analytics
Abstract: This paper introduces a comprehensive study aimed at enhancing the forecasting of perishable food item demand. Focusing on solving the critical issue of waste management within the supply chain of food products, the research undertakes a comparative analysis of various machine learning models. The development of an optimized model that is capable of accurately forecasting the demand for perishable food items is the focus of this research. The research includes …
Published in Journal of Software Engineering Tools & Technology Trends · Vol. 11, Issue 3, 2024 · pp. 27–37 Read article