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.

时间序列分类的研究在机器学习和数据挖掘领域越来越受到关注，这是因为时间序列数据几乎遍布世界各地，尤其是在我们的日常工作和生活中。最近的研究表明，卷积神经网络（CNN）可以从图像和文本中提取良好的特征，但是当直接将其用于解决时间序列分类问题时，通常会遇到精度较低的问题。在这种追求下，本研究设想了一种基于滑动相对位置矩阵和CNN的时间序列的新型组合模型。提出的模型首先采用幻灯片相对位置在预处理过程中将时间序列数据转换为2D图像，然后使用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性，从而在图像识别中利用了CNN的优势。最后，选择了14个UCR时间序列数据集来评估所提出模型的性能，其结果表明所提出模型的准确性高于其他模型。