Abstract

This paper presents a statistical feature approach in fully convolutional time series classification (TSC), which is aimed at improving the accuracy and efficiency of TSC. This method is based on fully convolutional neural networks (FCN), and there are the following two properties: statistical features in data preprocessing and fine-tuning strategies in network training. The key steps are described as follows: firstly, by the window slicing principle, dividing the original time series into multiple equal-length subsequences; secondly, by extracting statistical features on each subsequence, in order to form a new sequence as the input of the neural network, and training neural network by the fine-tuning idea; thirdly, by evaluating the classification performance about test sets; and finally, by comparing the sample sequence complexity and network classification loss accuracy with the FCN using the original sequence. Our experimental results show that the proposed method improved the classification effects of FCN and the residual network (ResNet), which means that it has a generalization ability to the network structures.

中文翻译：

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基于统计特征的时间序列分类

摘要

本文提出了一种全卷积时间序列分类（TSC）中的统计特征方法，旨在提高TSC的准确性和效率。该方法基于完全卷积神经网络（FCN），并且具有以下两个属性：数据预处理中的统计特征和网络训练中的微调策略。关键步骤描述如下：首先，根据窗口切片原理，将原始时间序列划分为多个等长子序列。其次，通过提取每个子序列的统计特征，形成新的序列作为神经网络的输入，并通过微调思想对神经网络进行训练。第三，通过评估测试集的分类性能；最后，通过使用原始序列将样本序列的复杂度和网络分类损失准确性与FCN进行比较。实验结果表明，该方法改善了FCN和残差网络（ResNet）的分类效果，这意味着它对网络结构具有泛化能力。