Multivariant Time-Series Forecasting Methodology for Product Demand Using Deep Learning and Large Language Models

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Multivariant Time-Series Forecasting Methodology for Product Demand Using Deep Learning and Large Language Models

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Computer Engineering, Vishwakarma University, Survey No. 2, 3, 4, Laxmi Nagar, Kondhwa Budruk, Pune 411 048, India
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Received: 06 September 2025 Accepted: 19 September 2025 Published: 29 September 2025

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© 2025 The authors. This is an open access article under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

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Intell. Sustain. Manuf. 2025, 2(2), 10028; DOI: 10.70322/ism.2025.10028
ABSTRACT: Accurate demand Soothsaying is a crucial element in force chain operation and business planning. Traditional statistical ways don’t consider the nonlinear, dynamic, and interdependent nature of variables that drive product demand, including deal history, prices, seasonality, elevations, request changes, and profitable pointers. This design presents a sophisticated soothsaying frame for guidance from an artificial intelligence system, integrating soothsaying using deep literacy models together with large language models(LLMs), that can negotiate both accurate soothsaying and give practicable intelligence. The deep literacy infrastructures used in this study include Long Short Term Memory(LSTM), Reopened intermittent Units(GRU), and other Motor models for timeseries soothsaying, which optimize temporal dependences and the complex cross-variable relations. To further increase interpretability of the vaticinations, LLMs are useful agents to convert the specialized cast affair into a completely automated and enhanced mortal-readable textbook and reports to develop intelligence for decision timber. Prophetic modeling and naturally generated reporting lead to better delicacy and practicable intelligence for their businesses. This intelligence empowers businesses to create better procurement processes, improve inventory management, and develop more resilient supply chains relevant to today’s business environment.
Keywords: Predictive analytics; Outlier detection; Trend analysis; Data-driven insights; Demand planning; Deep learning; LSTM; GRU; Transformer; TCN; Large language models; Multivariate time-series prediction; Forecast product demand
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