Attention-Based Deep Gated Fully Convolutional End-to-End Architectures for Time Series Classification

Time series classification (TSC) is one of the significant problems in the data mining community due to the wide class of domains involving the time series data. The TSC problem is being studied individually for univariate and multivariate using different datasets and methods. Subsequently, deep learning methods are more robust than other techniques and revolutionized many areas, including TSC. Therefore, in this study, we exploit the performance of attention mechanism, deep Gated Recurrent Unit (dGRU), Squeeze-and-Excitation (SE) block, and Fully Convolutional Network (FCN) in two end-to-end hybrid deep learning architectures, Att-dGRU-FCN and Att-dGRU-SE-FCN. The performance of the proposed models is evaluated in terms of classification testing error and f1-score. Extensive experiments and ablation study is carried out on multiple univariate and multivariate datasets from different domains to acquire the best performance of the proposed models. The proposed models show effective performance over other published methods, also do not require heavy data pre-processing, and small enough to be deployed on real-time systems.

Automated summary

This study investigates the time series classification problem using deep learning techniques, proposing two end-to-end deep learning architectures and validating them on multiple datasets. The results show that the proposed models perform well in terms of classification testing error and f1-score, outperforming other methods and being suitable for deployment on real-time systems.