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

作者：

Highlights：

• 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.

摘要

•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.

论文关键词：Autoencoder,Long short term memory networks,Anomaly detection,One-class SVM,Forecasting

论文评审过程：Received 31 December 2019, Revised 9 October 2020, Accepted 10 November 2020, Available online 16 December 2020, Version of Record 5 February 2021.

论文官网地址：https://doi.org/10.1016/j.ijinfomgt.2020.102282