论文标题
主题差异字段：用于分类时间序列的简单有效的图像表示
Motif Difference Field: A Simple and Effective Image Representation of Time Series for Classification
论文作者
论文摘要
时间序列图案在时间序列分析中起重要作用。基于基准的时间序列聚类用于发现时间序列数据中的高阶模式或结构。提出了基于时间序列的图像表示的卷积神经网络（CNN）分类器的启发，提出了基序差异字段（MDF）。与时间序列的其他图像表示相比，MDF简单易于构造。 MDF使用完全卷积网络（FCN）作为分类器，使用其他时间序列分类方法在基准中以基准的UCR时间序列数据集演示了出色的性能。有趣的是，三合会时间序列图案在测试中带来了最佳结果。由于MDF中反映的基序聚类，因此在梯度加权类激活映射（GRAD-CAM）的帮助下检测到显着的基序。在GRAD-CAM中重量高的MDF区域具有与时间序列中签名模式相关的所需序数模式的重要序列。但是，无法直接基于时间序列直接使用神经网络分类器来识别签名模式。
Time series motifs play an important role in the time series analysis. The motif-based time series clustering is used for the discovery of higher-order patterns or structures in time series data. Inspired by the convolutional neural network (CNN) classifier based on the image representations of time series, motif difference field (MDF) is proposed. Compared to other image representations of time series, MDF is simple and easy to construct. With the Fully Convolution Network (FCN) as the classifier, MDF demonstrates the superior performance on the UCR time series dataset in benchmark with other time series classification methods. It is interesting to find that the triadic time series motifs give the best result in the test. Due to the motif clustering reflected in MDF, the significant motifs are detected with the help of the Gradient-weighted Class Activation Mapping (Grad-CAM). The areas in MDF with high weight in Grad-CAM have a high contribution from the significant motifs with the desired ordinal patterns associated with the signature patterns in time series. However, the signature patterns cannot be identified with the neural network classifiers directly based on the time series.
