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Long Short Term Memory Fully Convolutional Network (LSTM-FCN) and  Attention LSTM-FCN (ALSTM-FCN) have been successful in classifying  univariate time series . However, they have never been applied to on a  multivariate time series classification problem. The models we  propose, Multivariate LSTM-FCN (MLSTM-FCN) and Multivariate.Attention  LSTM-FCN (MALSTM-FCN), converts their respective univariate models  into multivariate variants. We extend the squeeze-and-excite block to  the case of 1D sequence models and augment the fully convolutional  blocks of the LSTM-FCN and ALSTM-FCN models to enhance classification  accuracy.




As the datasets now consist of multivariate time series, we can define  a time series dataset as a tensor of shape (N, Q, M ), where N is the  number of samples in the dataset, Q is the maximum number of time  steps amongst all variables and M is the number of variables processed  per time step. Therefore a univariate time series dataset is a special  case of the above definition, where M is 1. The alteration required to  the input of the LSTM-FCN and ALSTM-FCN models is to accept M inputs  per time step, rather than a single input per time step.









Similar to LSTM-FCN and ALSTM-FCN, the proposed models comprise a  fully convolutional block and a LSTM block, as depicted in Fig. 1.  The fully convolutional block contains three temporal convolutional  blocks, used as a feature extractor, which is replicated from the  original fully convolutional block by Wang et al [34]. The  convolutional blocks contain a convolutional layer with a number of  filters (128, 256, and 128) and a kernel size of 8, 5, and 3  respectively. Each convolutional layer is succeeded by batch  normalization, with a momentum of 0.99 and epsilon of 0.001. The batch  normalization layer is succeeded by the ReLU activation function. In  addition, the first two convolutional blocks conclude 6 with a  squeeze-and-excite block, which sets the proposed model apart from  LSTM-FCN and ALSTM-FCN. Fig. 2 summarizes the process of how the  squeeze-and-excite block is computed in our architecture. For all  squeeze and excitation blocks, we set the reduction ratio r to 16. The  final temporal convolutional block is followed by a global average  pooling layer.The squeeze-and-excite block is an addition to the FCN block which adaptively recalibrates the input feature maps. Due to the reduction ratio r set to 16, the number of parameters required to learn these self-attention maps is reduced such that the overall model  size increases by just 3-10 %.








这个挤压-激活块是作为FCN块的补充,用于自适应重新校准feture map的权重值。由于监所比设置为16,学习这些自我注意图所需的参数数量减少,使得整个模型大小仅增加3-10%。


过滤器即卷积核 ,在卷积神经网络中根据其要提取的信息不同,可以有多个卷积和,卷积核大小即为过滤器大小




线性整流函数 (Linear rectification function),又称修正线性单元,是一种人工神经网络中常用的激活函数(activation function),通常指代以斜坡函数及其变种为代表的非线性函数。


This adaptive rescaling of the filter maps is of utmost importance to  the improved performance of the MLSTM-FCN model compared to LSTM-FCN,  as it incorporates learned self-attention to the inter-correlations  between multiple variables at each time step, which was inadequate  with the LSTM-FCN



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