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Explainable dual LSTM-autoencoders with exogenous features for anomaly detection and supply chain forecasting.

Chen Xiaoyang¹

¹Department of Business Administration, Shandong College of Economics and Business, Weifang, 261011, Shandong, China. chen_xiao_yang@yeah.net.

Scientific Reports

|November 27, 2025

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This study introduces an AI system for retail supply chains, enhancing demand forecasting and anomaly detection using Long Short-Term Memory (LSTM) networks and Autoencoders for improved accuracy and robustness.

Area of Science:

Supply Chain Management
Artificial Intelligence
Machine Learning

Background:

Retail systems face increasing complexity, necessitating advanced AI for optimal management.
Traditional statistical models struggle with nonlinear patterns, seasonality, and external factors in retail data.
Accurate demand prediction and anomaly detection are crucial for supply chain efficiency, cost reduction, and customer satisfaction.

Purpose of the Study:

To propose a novel dual-head AI system for simultaneous demand forecasting and anomaly detection in retail supply chains.
To enhance predictive accuracy and robustness against outliers through specialized network architectures and feature engineering.
To provide model transparency and interpretability for better understanding of forecasting and anomaly detection logic.

Main Methods:

Keywords:

Anomaly detection Deep learning Dual-head architecture Explainable AI LSTM-autoencoders Supply chain operations Time series forecasting

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A dual-head system combining Long Short-Term Memory (LSTM) networks for forecasting and Autoencoders for anomaly detection.
Implementation of feature engineering and selection techniques to capture temporal dependencies and structural patterns.
Empirical evaluation on the M5 Forecasting dataset using error-based, accuracy-based, and prediction interval metrics.

Main Results:

The proposed model achieved a 9.4% relative improvement over baseline deep learning models.
Demonstrated lower error rates with an RMSE of 10.84 and robust interval coverage with an MPIW of 5.2.
Model transparency was achieved using Saliency maps, SHAP values, and LIME explanations.

Conclusions:

The dual-head AI system significantly improves demand forecasting and anomaly detection in retail supply chains.
The integration of LSTM and Autoencoders, coupled with feature engineering, enhances model performance and reliability.
Explainable AI techniques provide valuable insights into model decision-making processes for supply chain management.