LSTM-Based Deep Learning Models for Long-Term Inventory Forecasting in Retail Operations

null null

doi:10.70393/6a6374616d.333238

OPEN ACCESS |Research Article ||4 November 2025

LSTM-Based Deep Learning Models for Long-Term Inventory Forecasting in Retail Operations

null null

Duke University

sichong.huang@alumni.duke.edu

Duke University, USA.

* Corresponding Author¹: null null, E-Mail: sichong.huang@alumni.duke.edu

Publication

Accepted 2025 October 18 ; Published 2025 November 4

Journal of Computer Technology and Applied Mathematics, 2025, 2(6), 3007-4126.

Abstract

Given the complex fluctuations and extended forecasting cycles inherent in retail inventory, this study investigates the application of LSTM deep learning models for inventory time series forecasting. It details the model architecture design, parameter optimization, and training methodology, while presenting the data construction and experimental validation process. Results demonstrate that the model effectively captures inventory variation patterns, enhances prediction accuracy and trend stability, and exhibits strong generalization capabilities across multiple forecasting horizons.

Keywords

Retail Inventory , LSTM Model , Time Series Forecasting , Parameter Optimization .

Metadata

DOI:

10.70393/6a6374616d.333238

ARK:

ark:/40704/JCTAM.v2n6a04

Pages: 21-25

References: 5

Disciplines: Artificial Intelligence

Subjects: Deep Learning

Cite This Article

APA Style

Unknown Author (2025). Lstm-based deep learning models for long-term inventory forecasting in retail operations. Journal of Computer Technology and Applied Mathematics, 2(6), 21-25. https://doi.org/10.70393/6a6374616d.333238

Acknowledgments

The authors thank the editor and anonymous reviewers for their helpful comments and valuable suggestions.

FUNDING

Not applicable.

INSTITUTIONAL REVIEW BOARD STATEMENT

Not applicable.

DATA AVAILABILITY STATEMENT

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

INFORMED CONSENT STATEMENT

Not applicable.

CONFLICT OF INTEREST

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

AUTHOR CONTRIBUTIONS

Not applicable.

References

1.

Wang, C., & Wang, J. (2025). Research on e-commerce inventory sales forecasting model based on ARIMA and LSTM algorithm. Mathematics, 13(11), 1838.

2.

Rong, L., & Vinay, V. (2024). Optimizing supply chain management through BO-CNN-LSTM for demand forecasting and inventory management. Journal of Organizational and End User Computing (JOEUC), 36(1), 1–25.

3.

Ming, Y. T., Yin, K. C., Yuiyip, L., et al. (2023). Data-intensive inventory forecasting with artificial intelligence models for cross-border e-commerce service automation. Applied Sciences, 13(5), 3051.

4.

Yang, H., & Yu, L. (2023). A method of forecasting cross-border e-commerce stocking for SMEs based on demand characteristics and sequence trends under sustainable development strategy. International Journal of Computational Systems Engineering, 7(2–4), 57–66.

5.

Myungsoo, K., Jaehyeong, L., Chaegyu, L., et al. (2022). Framework of 2D KDE and LSTM-based forecasting for cost-effective inventory management in smart manufacturing. Applied Sciences, 12(5), 2380.

PUBLISHER'S NOTE

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Copyright © 2025 The Author(s). Published by Southern United Academy of Sciences.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.