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

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

Supplementary Materials

The following supporting information can be found at: https://www.sciepublish.com/article/pii/704, The full dataset is available as supplementary material at https://github.com/Pranav4555/csv.

Statement of the Use of Generative AI and AI-Assisted Technologies in the Writing Process

The authors employed OpenAI's ChatGPT to help with language polishing, grammar correction, and sentence restructuring for improved readability while preparing this manuscript. To make sure the output was accurate, unique, and in line with the intended meaning, the authors thoroughly examined and revised it. The authors alone bear ultimate responsibility for the content of this manuscript.

Acknowledgment

As the authors of this project, we would like to sincerely acknowledge Seema Vanjire for her unwavering support and guidance throughout our research. We would also like to thank Vishwakarma University, Department of Computer Engineering for the resources provided to us.

Author Contributions

Conceptualization, P.B. and D.P.; Methodology, P.D. and A.K.G.; Software, A.K.; Validation, P.B., D.P. and P.D.; Formal Analysis, P.B.; Investigation, A.K.G. and A.K.; Data Curation, P.D. and A.K.G.; Writing—Original Draft Preparation, D.P. and A.K.; Writing—Review & Editing, S.V.; Visualization, P.B.; Resources, S.V.; Supervision, S.V.; Project Administration, S.V.

Ethics Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Upon reasonable request, the corresponding author will provide the data that support the study’s conclusions.

Funding

This research received no external funding.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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