The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management
Open accessAbstract
Abstract 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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An Introduction to Variational Autoencoders
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Time-series forecasting with deep learning: a survey
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A Comparison of ARIMA and LSTM in Forecasting Time Series
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Li, G., & Jung, J. J. (2023) · Information Fusion · doi:10.1016/j.inffus.2022.10.008 · Cited by 361
Dey, P. K., Chowdhury, S., Abadie, A., Vann Yaroson, E., & Sarkar, S. (2024) · International Journal of Production Research · doi:10.1080/00207543.2023.2179859 · Cited by 330
LSTM-Autoencoder-Based Anomaly Detection for Indoor Air Quality Time-Series Data
Wei, Y., Jang-Jaccard, J., Xu, W., Sabrina, F., Camtepe, S., & Boulic, M. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3230361 · Cited by 269
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Metadata
- Authors
- Nguyen, H. D., Tran, K. P., Thomassey, S., & Hamad, M.
- Venue
- International Journal of Information Management
- Publisher
- Elsevier BV
- Published
- 2021-04
- Volume
- 57
- Pages
- 102282
- ISSN
- 0268-4012 (ISSN-L)
- OpenAlex
- W3111082827
- Cited by
- 494
- References
- 34
- FWCI
- 35.02
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Nguyen, H. D., Tran, K. P., Thomassey, S., & Hamad, M. (2021). Forecasting and Anomaly Detection approaches using LSTM and LSTM Autoencoder techniques with the applications in supply chain management. International Journal of Information Management. https://doi.org/10.1016/j.ijinfomgt.2020.102282
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