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arXiv · 1709.05206

LSTM Fully Convolutional Networks for Time Series Classification

Fazle Karim·Somshubra Majumdar·Houshang Darabi·Shun Chen
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Abstract

Fully convolutional neural networks (FCN) have been shown to achieve 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 dataset. The proposed Long Short Term Memory Fully Convolutional Network (LSTM-FCN) achieves state-of-the-art performance compared to 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). Utilization of the attention mechanism allows one to visualize the decision process of the LSTM cell. Furthermore, we propose fine-tuning as a method to enhance the performance of trained models. An overall analysis of the performance of our model is provided and compared to other techniques.

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Fazle Karim, Somshubra Majumdar, Houshang Darabi, Shun Chen. 2017-09-08. LSTM Fully Convolutional Networks for Time Series Classification. https://doi.org/10.1109/access.2017.2779939

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