Wang Junlu, Li Su, Ji Wanting, Jiang Tian, Song Baoyan
School of Information, Liaoning University, Shenyang, 110036, China.
Sci Rep. 2022 Sep 21;12(1):15731. doi: 10.1038/s41598-022-19758-5.
Time series classification is a basic task in the field of streaming data event analysis and data mining. The existing time series classification methods have the problems of low classification accuracy and low efficiency. To solve these problems, this paper proposes a T-CNN time series classification method based on a Gram matrix. Specifically, we perform wavelet threshold denoising on time series to filter normal curve noise, and propose a lossless transformation method based on the Gram matrix, which converts the time series to the time domain image and retains all the information of events. Then, we propose an improved CNN time series classification method, which introduces the Toeplitz convolution kernel matrix into convolution layer calculation. Finally, we introduce a Triplet network to calculate the similarity between similar events and different classes of events, and optimize the squared loss function of CNN. The proposed T-CNN model can accelerate the convergence rate of gradient descent and improve classification accuracy. Experimental results show that, compared with the existing methods, our T-CNN time series classification method has great advantages in efficiency and accuracy.
时间序列分类是流数据事件分析和数据挖掘领域的一项基本任务。现有的时间序列分类方法存在分类准确率低和效率低的问题。为了解决这些问题，本文提出了一种基于Gram矩阵的T-CNN时间序列分类方法。具体来说，我们对时间序列进行小波阈值去噪以滤除正常曲线噪声，并提出一种基于Gram矩阵的无损变换方法，该方法将时间序列转换为时域图像并保留事件的所有信息。然后，我们提出了一种改进的CNN时间序列分类方法，该方法将Toeplitz卷积核矩阵引入卷积层计算。最后，我们引入一个三元组网络来计算相似事件和不同类事件之间的相似度，并优化CNN的平方损失函数。所提出的T-CNN模型可以加快梯度下降的收敛速度并提高分类准确率。实验结果表明，与现有方法相比，我们的T-CNN时间序列分类方法在效率和准确率方面具有很大优势。
