Murai Tatsumasa, Koga Hisashi
Department of Computer and Network Engineering, University of Electro-Communications, Tokyo 182-8585, Japan.
Entropy (Basel). 2023 Jun 19;25(6):953. doi: 10.3390/e25060953.
As the Internet-of-Things is deployed widely, many time-series data are generated everyday. Thus, classifying time-series automatically has become important. Compression-based pattern recognition has attracted attention, because it can analyze various data universally with few model parameters. RPCD (Recurrent Plots Compression Distance) is known as a compression-based time-series classification method. First, RPCD transforms time-series data into an image called "Recurrent Plots (RP)". Then, the distance between two time-series data is determined as the dissimilarity between their RPs. Here, the dissimilarity between two images is computed from the file size, when an MPEG-1 encoder compresses the video, which serializes the two images in order. In this paper, by analyzing the RPCD, we give an important insight that the quality parameter for the MPEG-1 encoding that controls the resolution of compressed videos influences the classification performance very much. We also show that the optimal parameter value depends extremely on the dataset to be classified: Interestingly, the optimal value for one dataset can make the RPCD fall behind a naive random classifier for another dataset. Supported by these insights, we propose an improved version of RPCD named qRPCD, which searches the optimal parameter value by means of cross-validation. Experimentally, qRPCD works superiorly to the original RPCD by about 4% in terms of classification accuracy.
随着物联网的广泛部署，每天都会生成大量时间序列数据。因此，自动对时间序列进行分类变得至关重要。基于压缩的模式识别受到关注，因为它可以用很少的模型参数对各种数据进行通用分析。RPCD（递归图压缩距离）是一种基于压缩的时间序列分类方法。首先，RPCD将时间序列数据转换为一种称为“递归图（RP）”的图像。然后，将两个时间序列数据之间的距离确定为它们的RP之间的差异。这里，当MPEG-1编码器压缩视频时，从文件大小计算两个图像之间的差异，该视频按顺序对这两个图像进行序列化。在本文中，通过分析RPCD，我们得到一个重要的见解，即控制压缩视频分辨率的MPEG-1编码的质量参数对分类性能有很大影响。我们还表明，最佳参数值极大地依赖于要分类的数据集：有趣的是，一个数据集的最佳值可能会使RPCD在另一个数据集上落后于简单的随机分类器。基于这些见解，我们提出了RPCD的改进版本qRPCD，它通过交叉验证来搜索最佳参数值。实验表明，在分类准确率方面，qRPCD比原始RPCD高出约4%。
