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Título
Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder
Autor
Facultad/Centro
Área de conocimiento
Título de la revista
Integrated Computer-Aided Engineering
Número de la revista
2
Datos de la obra
Alonso, S., Morán, A., Pérez, D., Prada, M. A., Fuertes, J. J., & Domínguez, M. (2024). Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder. Integrated Computer-Aided Engineering, 31(2), 157-172. https://doi.org/10.3233/ICA-230728
Editor
IOS Press
Fecha
2024-01-30
ISSN
1069-2509
Abstract
[EN] Technological advances in industry have made it possible to install many connected sensors, generating a great amount of observations at high rate. The advent of Industry 4.0 requires analysis capabilities of heterogeneous data in form of related multivariate time series. However, missing data can degrade processing and lead to bias and misunderstandings or even wrong decision-making. In this paper, a recurrent neural network-based denoising autoencoder is proposed for gap imputation in related multivariate time series, i.e., series that exhibit spatio-temporal correlations. The denoising autoencoder (DAE) is able to reproduce input missing data by learning to remove intentionally added gaps, while the recurrent neural network (RNN) captures temporal patterns and relationships among variables. For that reason, different unidirectional (simple RNN, GRU, LSTM) and bidirectional (BiSRNN, BiGRU, BiLSTM) architectures are compared with each other and to state-of-the-art methods using three different datasets in the experiments. The implementation with BiGRU layers outperforms the others, effectively filling gaps with a low reconstruction error. The use of this approach is appropriate for complex scenarios where several variables contain long gaps. However, extreme scenarios with very short gaps in one variable or no available data should be avoided. © 2024 - The authors. Published by IOS Press.
Materia
Palabras clave
Peer review
SI
ID proyecto
- info:eu-repo/grantAgreement/AEI/Programa Estatal de I+D+i Orientada a los Retos de la Sociedad/PID2020-117890RB-I00 AEI 2020/ES/TECNICAS DE MODELADO INTELIGENTE BASADO EN DATOS APLICADAS A INSTALACIONES INDUSTRIALES PARA LA MEJORA DE LA EFICIENCIA ENERGETICA
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