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

Sichong Huang

doi:10.70393/6a6374616d.333238

Authors

Sichong Huang Duke University

DOI:

https://doi.org/10.70393/6a6374616d.333238

ARK:

https://n2t.net/ark:/40704/JCTAM.v2n6a04

Disciplines:

Artificial Intelligence

Subjects:

Deep Learning

References:

5

Keywords:

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

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.

Author Biography

Sichong Huang, Duke University

Duke University, USA.

References

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