Bidirectional LSTM-RNN-based hybrid deep learning frameworks for univariate time series classification

Time series classification (TSC) has been around for recent decades as a significant research problem for industry practitioners as well as academic researchers. Due to the rapid increase in temporal data in a wide range of disciplines, an incredible amount of algorithms have been proposed. This pap...

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Vydané v:The Journal of supercomputing Ročník 77; číslo 7; s. 7021 - 7045
Hlavní autori: Khan, Mehak, Wang, Hongzhi, Riaz, Adnan, Elfatyany, Aya, Karim, Sajida
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: New York Springer US 01.07.2021
Springer Nature B.V
Predmet:
Algorithms
Art techniques
Classification
Compilers
Computer Science
Deep learning
Feature extraction
Interpreters
Machine learning
Processor Architectures
Programming Languages
Time series
Deep learning
Attention mechanism
Bidirectional long short-term memory recurrent neural network
Time series classification
Convolutional neural network
ISSN:0920-8542, 1573-0484
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Shrnutí:Time series classification (TSC) has been around for recent decades as a significant research problem for industry practitioners as well as academic researchers. Due to the rapid increase in temporal data in a wide range of disciplines, an incredible amount of algorithms have been proposed. This paper proposes robust approaches based on state-of-the-art techniques, bidirectional long short-term memory (BiLSTM), fully convolutional network (FCN), and attention mechanism. A BiLSTM considers both forward and backward dependencies, and FCN is proven to be good at feature extraction as a TSC baseline. Therefore, we augment BiLSTM and FCN in a hybrid deep learning architecture, BiLSTM-FCN. Moreover, we similarly explore the use of the attention mechanism to check its efficiency on BiLSTM-FCN and propose another model ABiLSTM-FCN. We validate the performance on 85 datasets from the University of California Riverside (UCR) univariate time series archive. The proposed models are evaluated in terms of classification testing error and f1-score and also provide performance comparison with various existing state-of-the-art techniques. The experimental results show that our proposed models perform comprehensively better than the existing state-of-the-art methods and baselines.
Bibliografia:ObjectType-Article-1
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-020-03560-z