A Novel Embedded Discretization-Based Deep Learning Architecture for Multivariate Time Series Classification

• Published in: IEEE transactions on industrial informatics 2023-04, Vol.19 (4), p.5976-5984
• Main Authors: Tahan, Marzieh Hajizadeh, Ghasemzadeh, Mohammad, Asadi, Shahrokh
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Classification; Convolutional neural network; Data models; Deep learning; Discretization; Evaluation; Informatics; Kernel; long short term memory (LSTM); Machine learning; Merging; Multivariate analysis; multivariate time series; temporal discretization; Time series; Time series analysis; time series classification (TSC); time series discretization; Training
• ISSN: 1551-3203; 1941-0050
• DOI: 10.1109/TII.2022.3188839

Deep learning-based time series classification techniques have significantly improved in recent years. While previous works have mentioned the fundamental importance of temporal discretization, most studies focus on improving model architectures. In this article, several models have been presented that use temporal discretization as a step of preprocessing time series and embed it in the deep neural network. The proposed models consist of two parts: temporal discretization and model training. The first part does the task of discretization and partially the selection of primary features, and the second part does the job of selecting more accurate features and classification. For this purpose, two loss functions have been used: a loss function to evaluate the discretization quality and the other one to evaluate the classification accuracy. The evaluation of the proposed models using 20 benchmarks multivariate time series shows that the proposed methods are more accurate than the state-of-the-art methods.