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

•Two data-driven approached are proposed to enhance decision making better in supply chain.•A multivariate time series forecasting is performed with a Long Short Term Memory (LSTM) network based method.•A LSTM Autoencoder network-based method combined with a one-class support vector machine.•The pro...

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Veröffentlicht in:	International journal of information management Jg. 57; S. 102282
Hauptverfasser:	Nguyen, H.D., Tran, K.P., Thomassey, S., Hamad, M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.04.2021 Elsevier Science Ltd
Schlagworte:	Algorithms Anomalies Anomaly detection Autoencoder Companies Data Datasets Decision making Decisions Forecasting Long short term memory networks Multivariate analysis One-class SVM Optimization Sales Short term memory Social networks Space technology Supply Supply chain management Supply chains Support vector machines Time series One-class SVM Long short term memory networks Autoencoder Forecasting Anomaly detection
ISSN:	0268-4012, 1873-4707
Online-Zugang:	Volltext
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