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Hybrid Deep-Learning Frameworks for Prediction of Industrial Material Demands
Subject area: Science,Engineering and Technology · Area of research: Deep Learning for Demand Forecasting
DOI: https://doi.org/10.64388/IREV9I9-1715115
Abstract
This research introduces an advanced hybrid forecasting framework designed to enhance the accuracy of industrial material demand prediction. The proposed system integrates deep-learning architectures with the principles of chaos theory to effectively model complex temporal and nonlinear dependencies in industrial datasets. By combining Chaotic Long Short-Term Memory (LSTM) and Chaotic N-BEATS networks, the framework captures intricate seasonal and dynamic demand variations. The dataset utilized includes both historical sales and environmental attributes such as temperature, humidity, precipitation, and activity metrics to provide a comprehensive understanding of consumption trends. Rigorous preprocessing and feature engineering techniques were applied to ensure high data quality. Performance was evaluated using metrics such as MAE, MSE, RMSE, MAPE, accuracy, and training time. Experimental results reveal that the hybrid chaotic models deliver superior predictive performance and improved generalization capability, making the proposed approach a valuable tool for intelligent supply chain planning and inventory management.
Keywords
Material Demand Forecasting, Chaotic LSTM, Chaotic N-BEATS, Time Series Forecasting, Deep Learning, Chaos Theory, Feature Engineering, Environmental Factors, Supply Chain Optimization.
References
[1] Authors of Procedia Computer Science (2022), “Predictive Analytics for Demand Forecasting – A Comparison of SARIMA and LSTM in Retail SCM”, in ResearchGate,
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[4] Chunrui Lei, Heng Zhang, Zhigang Wang, Qiang Miao, “Deep Learning for Demand Forecasting: A Framework Incorporating Variational Mode Decomposition and Attention Mechanism”, Processes, Vol. 13(2):594, 2025.
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How to cite this paper
@article{1715115,
author = {Karthika K, Kaviya J, Lalitha Rani U, U M Ramya},
title = {Hybrid Deep-Learning Frameworks for Prediction of Industrial Material Demands},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {9},
pages = {924-928},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715115.pdf},
abstract = {This research introduces an advanced hybrid forecasting framework designed to enhance the accuracy of industrial material demand prediction. The proposed system integrates deep-learning architectures with the principles of chaos theory to effectively model complex temporal and nonlinear dependencies in industrial datasets. By combining Chaotic Long Short-Term Memory (LSTM) and Chaotic N-BEATS networks, the framework captures intricate seasonal and dynamic demand variations. The dataset utilized includes both historical sales and environmental attributes such as temperature, humidity, precipitation, and activity metrics to provide a comprehensive understanding of consumption trends. Rigorous preprocessing and feature engineering techniques were applied to ensure high data quality. Performance was evaluated using metrics such as MAE, MSE, RMSE, MAPE, accuracy, and training time. Experimental results reveal that the hybrid chaotic models deliver superior predictive performance and improved generalization capability, making the proposed approach a valuable tool for intelligent supply chain planning and inventory management.},
keywords = {Material Demand Forecasting, Chaotic LSTM, Chaotic N-BEATS, Time Series Forecasting, Deep Learning, Chaos Theory, Feature Engineering, Environmental Factors, Supply Chain Optimization.},
month = {March},
doi = {https://doi.org/10.64388/IREV9I9-1715115}
}