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LSTM Fully Convolutional Networks for Time Series Classification
Open accessAbstract
Fully convolutional neural networks (FCNs) have been shown to achieve the state-of-the-art performance on the task of classifying time series sequences. We propose the augmentation of fully convolutional networks with long short term memory recurrent neural network (LSTM RNN) sub-modules for time series classification. Our proposed models significantly enhance the performance of fully convolutional networks with a nominal increase in model size and require minimal preprocessing of the data set. The proposed long short term memory fully convolutional network (LSTM-FCN) achieves the state-of-the-art performance compared with others. We also explore the usage of attention mechanism to improve time series classification with the attention long short term memory fully convolutional network (ALSTM-FCN). The attention mechanism allows one to visualize the decision process of the LSTM cell. Furthermore, we propose refinement as a method to enhance the performance of trained models. An overall analysis of the performance of our model is provided and compared with other techniques.
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Metadata
- Authors
- Karim, F., Majumdar, S., Darabi, H., & Chen, S.
- Venue
- IEEE Access
- Published
- 2018
- Volume
- 6
- Pages
- 1662-1669
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2754051771
- Cited by
- 1,465
- References
- 10
- FWCI
- 72.15
- Type
- journal article
- Language
- en
- Open access
- gold
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