Titre :

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

Auteur(s) :

Nguyen, H. D. [Auteur]
Institute of Artificial Intelligence and Data Science
Génie et Matériaux Textiles [GEMTEX]
Tran, Kim-Phuc [Auteur]
Génie et Matériaux Textiles [GEMTEX]
École nationale supérieure des arts et industries textiles [ENSAIT]
Thomassey, S. [Auteur]
Génie et Matériaux Textiles [GEMTEX]
Hamad, M. [Auteur]

Titre de la revue :

International Journal of Information Management

Nom court de la revue :

Int. J. Inf. Manage.

Numéro :

57

Pagination :

-

Date de publication :

2020-11-25

ISSN :

0268-4012

Mot(s)-clé(s) en anglais :

Autoencoder
Long short term memory networks
Anomaly detection
One-class SVM
Forecasting

Discipline(s) HAL :

Sciences de l'ingénieur [physics]

Résumé en anglais : [en]

Making appropriate decisions is indeed a key factor to help companies facing challenges from supply chains nowadays. In this paper, we propose two datadriven approaches that allow making better decisions in supply chain ...
Lire la suite >Making appropriate decisions is indeed a key factor to help companies facing challenges from supply chains nowadays. In this paper, we propose two datadriven approaches that allow making better decisions in supply chain management. In particular, we suggest a Long Short Term Memory (LSTM) networkbased 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 some other methods based on a dataset provided by NASA.Lire moins >

Langue :

Anglais

Audience :

Internationale

Vulgarisation :

Non

Établissement(s) :

Université de Lille
ENSAIT
Junia HEI

Collections :

• Génie des matériaux textiles (GEMTEX) - ULR 2641

Date de dépôt :

2023-06-20T11:43:13Z
2024-03-14T09:37:53Z