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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| Vydáno v: | International journal of information management Ročník 57; s. 102282 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Kidlington
Elsevier Ltd
01.04.2021
Elsevier Science Ltd |
| Témata: | |
| ISSN: | 0268-4012, 1873-4707 |
| On-line přístup: | Získat plný text |
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| Abstract | •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. |
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| AbstractList | •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. 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. |
| ArticleNumber | 102282 |
| Author | Hamad, M. Nguyen, H.D. Thomassey, S. Tran, K.P. |
| Author_xml | – sequence: 1 givenname: H.D. surname: Nguyen fullname: Nguyen, H.D. organization: Institute of Artificial Intelligence and Data Science, Dong A University, Da Nang, Viet Nam – sequence: 2 givenname: K.P. surname: Tran fullname: Tran, K.P. email: kim-phuc.tran@ensait.fr organization: ENSAIT, GEMTEX - Laboratoire de Génie et Matériaux Textiles, Lille, F-59000, France – sequence: 3 givenname: S. surname: Thomassey fullname: Thomassey, S. organization: ENSAIT, GEMTEX - Laboratoire de Génie et Matériaux Textiles, Lille, F-59000, France – sequence: 4 givenname: M. surname: Hamad fullname: Hamad, M. organization: CEO Driven, 54 Rue Norbert Segard, Hem, 59510, France |
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| Snippet | •Two data-driven approached are proposed to enhance decision making better in supply chain.•A multivariate time series forecasting is performed with a Long... 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... |
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| SubjectTerms | 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 |
| Title | Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management |
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