SRPM–CNN: a combined model based on slide relative position matrix and CNN for time series classification
SRPM–CNN: a combined model based on slide relative position matrix and CNN for time series classification
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DOI：
10.1007/s40747-021-00296-y
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发表时间：
2021-02
影响因子：
5.8
通讯作者：
Taoying Li;Yuqi Zhang;Tingniao Wang
中科院分区：
文献类型：
--
作者：
Taoying Li;Yuqi Zhang;Tingniao Wang
由于时间序列数据几乎无处不在，特别是在我们的日常工作和生活中，对时间序列分类的研究越来越受到机器学习和数据挖掘领域的关注。近年来的研究表明，卷积神经网络（CNN）可以从图像和文本中提取出很好的特征，但在直接用于解决时间序列分类问题时，往往会遇到准确率不高的问题。为此，本研究设想了一种基于滑动相对位置矩阵和时间序列CNN的新型组合模型。该模型首先在预处理过程中采用滑动相对位置将时间序列数据转换为二维图像，然后利用CNN对这些图像进行分类。这充分利用了时间序列数据的时间序列特征，从而发挥了CNN在图像识别中的优势。最后，选择14个UCR时间序列数据集对所提模型的性能进行评价，结果表明所提模型的准确率高于其他模型。
Research on the time series classification is gaining an increased attention in the machine learning and data mining areas due to the existence of the time series data almost everywhere, especially in our daily work and life. Recent studies have shown that the convolutional neural networks (CNN) can extract good features from the images and texts, but it often encounters the problem of low accuracy, when it is directly employed to solve the problem of time series classification. In this pursuit, the present study envisaged a novel combined model based on the slide relative position matrix and CNN for time series. The proposed model first adopted the slide relative position for converting the time series data into 2D images during preprocessing, and then employed CNN to classify these images. This made the best of the temporal sequence characteristic of time series data, thereby utilizing the advantages of CNN in image recognition. Finally, 14 UCR time series datasets were chosen to evaluate the performance of the proposed model, whose results indicate that the accuracy of the proposed model was higher than others.