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Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management

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  • Nguyen, H.D.
  • Tran, K.P.
  • Thomassey, S.
  • Hamad, M.

Abstract

Making appropriate decisions is indeed a key factor to help companies facing challenges from supply chains nowadays. In this paper, we propose two data-driven approaches that allow making better decisions in supply chain management. In particular, we suggest a Long Short Term Memory (LSTM) network-based method for forecasting multivariate time series data and an LSTM Autoencoder network-based method combined with a one-class support vector machine algorithm for detecting anomalies in sales. Unlike other approaches, we recommend combining external and internal company data sources for the purpose of enhancing the performance of forecasting algorithms using multivariate LSTM with the optimal hyperparameters. In addition, we also propose a method to optimize hyperparameters for hybrid algorithms for detecting anomalies in time series data. The proposed approaches will be applied to both benchmarking datasets and real data in fashion retail. The obtained results show that the LSTM Autoencoder based method leads to better performance for anomaly detection compared to the LSTM based method suggested in a previous study. The proposed forecasting method for multivariate time series data also performs better than some other methods based on a dataset provided by NASA.

Suggested Citation

  • Nguyen, H.D. & Tran, K.P. & Thomassey, S. & Hamad, M., 2021. "Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management," International Journal of Information Management, Elsevier, vol. 57(C).
    • Handle: RePEc:eee:ininma:v:57:y:2021:i:c:s026840122031481x
    DOI: 10.1016/j.ijinfomgt.2020.102282
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    Cited by:

    1. Costa, Nahuel & Sánchez, Luciano, 2022. "Variational encoding approach for interpretable assessment of remaining useful life estimation," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    2. Rameshwar Garg & Shriya Barpanda & Girish Rao Salanke N S & Ramya S, 2022. "Machine Learning Algorithms for Time Series Analysis and Forecasting," Papers 2211.14387, arXiv.org.
    3. Li, Shicheng & Huang, Xiaoyong & Cheng, Zhonghou & Zou, Wei & Yi, Yugen, 2023. "AE-ACG: A novel deep learning-based method for stock price movement prediction," Finance Research Letters, Elsevier, vol. 58(PA).
    4. Mahmoud Ashraf & Amr Eltawil & Islam Ali, 2024. "Disruption detection for a cognitive digital supply chain twin using hybrid deep learning," Operational Research, Springer, vol. 24(2), pages 1-31, June.
    5. Li, Tong & Wang, Zhaohua & Zhao, Wenhui, 2022. "Comparison and application potential analysis of autoencoder-based electricity pattern mining algorithms for large-scale demand response," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    6. Hao Lin & Guannan Liu & Junjie Wu & J. Leon Zhao, 2024. "Deterring the Gray Market: Product Diversion Detection via Learning Disentangled Representations of Multivariate Time Series," INFORMS Journal on Computing, INFORMS, vol. 36(2), pages 571-586, March.
    7. Stover, Oliver & Nath, Paromita & Karve, Pranav & Mahadevan, Sankaran & Baroud, Hiba, 2024. "Dependence structure learning and joint probabilistic forecasting of stochastic power grid variables," Applied Energy, Elsevier, vol. 357(C).
    8. Rizeakos, V. & Bachoumis, A. & Andriopoulos, N. & Birbas, M. & Birbas, A., 2023. "Deep learning-based application for fault location identification and type classification in active distribution grids," Applied Energy, Elsevier, vol. 338(C).

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