LSTM Fully Convolutional Networks for Time Series Classification

Details

• Personal Author:
  Chen S ; Darabi H ; Karim F ; Majumdar S
• Description:
  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. [Description provided by NIOSH]
• Subjects:
  Occupational Health Safety Software
• Keywords:
  Author Keywords: Convolutional Neural Network Computer Applications Computer Software Industrial Engineering Long Short Term Memory Recurrent Neural Network Neural Networks Time Series Classification
• ISSN:
  2169-3536
• Document Type:
  Text
• Funding:
  Grant
• Genre:
  Journal Article
• Place as Subject:
  Illinois ; OSHA Region 5
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Volume:
  6
• NIOSHTIC Number:
  nn:20063552
• Citation:
  IEEE Access 2017 Dec; 6:1662-1669
• Contact Point Address:
  Houshang Darabi, Mechanical and Industrial Engineering, University of Illinois at Chicago, Chicago, IL 60607, USA
• Email:
  hdarabi@uic.edu
• Federal Fiscal Year:
  2018
• Performing Organization:
  University of Illinois at Chicago
• Peer Reviewed:
  True
• Start Date:
  20050701
• Source Full Name:
  IEEE Access
• End Date:
  20290630
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:39bc3fd47dbe34a040bb5cb87d9dce180a1384b9a17ebf9f7efbf89a4db6f2898161a7ee8a05bcbe0037efc655c39197e16c4bc52c4940c00f5b95f16fe66de1
• Download URL:
  https://stacks.cdc.gov/view/cdc/222140/cdc_222140_DS1.pdf
• File Type:
  [PDF - 5.35 MB ]