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...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
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
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:•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 proposed approach is implemented to both benchmarking and real datasets. 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.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0268-4012
1873-4707
DOI:10.1016/j.ijinfomgt.2020.102282