LSTM Fully Convolutional Networks for Time Series Classification

Code

• github.com/titu1994/LSTM-FCN
  Officialtf★ 804
• github.com/houshd/LSTM-FCN
  In papertf★ 139
• github.com/timeseriesAI/tsai/tree/main/tsai/models
  pytorch★ 0
• github.com/phuijse/MATIC
  pytorch★ 0
• github.com/houshd/MLSTM-FCN
  tf★ 0
• github.com/roytalman/LSTM-FCN-Pytorch
  pytorch★ 0
• github.com/titu1994/MLSTM-FCN
  tf★ 0
• github.com/flaviagiammarino/lstm-fcn-pytorch
  pytorch★ 0
• github.com/kmutya/Algorithms-for-Drowsy-Driver-Detection
  none★ 0

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.

Tasks

Benchmark Results

Reproductions