Over the past decade, multivariate time series classification has received
great attention. We propose transforming the existing univariate time series
classification models, the Long Short Term Memory Fully Convolutional Network
(LSTM-FCN) and Attention LSTM-FCN (ALSTM-FCN), into a multivariate time series
classification model by augmenting the fully convolutional block with a
squeeze-and-excitation block to further improve accuracy. Our proposed models
outperform most state-of-the-art models while requiring minimum preprocessing.
The proposed models work efficiently on various complex multivariate time
series classification tasks such as activity recognition or action recognition.
Furthermore, the proposed models are highly efficient at test time and small
enough to deploy on memory constrained systems.

本研究将现有的单变量时间序列分类模型 LSTM-FCN 和 ALSTM-FCN 转化为多变量时间序列分类模型，通过增加 squeeze-and-excitation 块改善了分类的准确性。提出的模型适用于各种复杂的多变量时间序列分类任务，如活动识别或动作识别。此外，提出的模型在测试时非常高效，且足够小以部署在内存受限的系统上。