Forecasting Commodity Prices Using Deep Learning Techniques: An Empirical Evidence from India

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]12/09/2025 at 12:01 PM[/if 2224] | [if 1553 equals=””] Volume : 16 [else] Volume : 16[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page : 08 12

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    Amrinder Kaur, Archana Goel,

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  1. Research Scholar, Associate Professor, Department of Marketing and Finance Programme, Chitkara Business School, Chitkara University, Department of Marketing and Finance Programme, Chitkara Business School, Chitkara University, Punjab, Punjab, India, India

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Abstract

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nCommodity 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, focusing on the Indian market. It highlights existing research gaps such as insufficient exploration of hyperparameter optimization, lack of risk-averse strategies, and limited integration of macroeconomic indicators and financial news sentiment analysis. Furthermore, most studies rely on short-term datasets, overlooking seasonal and annual trends that significantly influence commodity prices. By examining various deep learning models, including LSTM, Convolutional Neural Networks (CNN), and Reinforcement Learning (RL), this study aims to identify the most effective models for forecasting Indian commodity prices. Additionally, it compares deep learning methods with traditional forecasting techniques such as ARIMA and GARCH. The findings suggest that deep learning models, particularly that incorporating sentiment analysis from financial news, exceed the performance of conventional models in forecasting precision and economic efficiency. The study also emphasizes the need for interval forecasting to provide more comprehensive insights for investors and policymakers. Future research should focus on refining model architectures, incorporating broader datasets, and integrating hybrid models to optimize predictive performance.nn

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Keywords: Commodity price forecasting, deep learning (DL), Indian commodity market, machine learning (ML), financial markets, time series analysis, sentiment analysis

n[if 424 equals=”Regular Issue”][This article belongs to Journal of Computer Technology & Applications ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Journal of Computer Technology & Applications (jocta)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nAmrinder Kaur, Archana Goel. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Forecasting Commodity Prices Using Deep Learning Techniques: An Empirical Evidence from India[/if 2584]. Journal of Computer Technology & Applications. 07/08/2025; 16(03):08-12.

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How to cite this URL:
nAmrinder Kaur, Archana Goel. [if 2584 equals=”][226 striphtml=1][else]Forecasting Commodity Prices Using Deep Learning Techniques: An Empirical Evidence from India[/if 2584]. Journal of Computer Technology & Applications. 07/08/2025; 16(03):08-12. Available from: https://journals.stmjournals.com/jocta/article=07/08/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Volume 16
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 28/04/2025
Accepted 10/07/2025
Published 07/08/2025
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Publication Time 101 Days

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