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.
Citations
IRE Journals:
Karthika K, Kaviya J, Lalitha Rani U, U M Ramya "Hybrid Deep-Learning Frameworks for Prediction of Industrial Material Demands" Iconic Research And Engineering Journals Volume 9 Issue 9 2026 Page 924-928 https://doi.org/10.64388/IREV9I9-1715115
IEEE:
Karthika K, Kaviya J, Lalitha Rani U, U M Ramya
"Hybrid Deep-Learning Frameworks for Prediction of Industrial Material Demands" Iconic Research And Engineering Journals, 9(9) https://doi.org/10.64388/IREV9I9-1715115
About IRE Journals
IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
Important Links
For Authors
Volume 8:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 7:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 6:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 5:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 4:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12Volume 3:
Issue 1, Issue 2, Issue 3, Issue 4, Issue 5, Issue 6, Issue 7, Issue 8, Issue 9, Issue 10, Issue 11, Issue 12