In current research, machine and deep learning solutions for the classification of temporal data are shifting from single-channel datasets (univariate) to problems with multiple channels of information (multivariate). The majority of these works are focused on the method novelty and architecture, and the format of the input data is often treated implicitly. Particularly, multivariate datasets are often treated as a stack of univariate time series in terms of input preprocessing, with scaling methods applied across each channel separately. In this evaluation, we aim to demonstrate that the additional channel dimension is far from trivial and different approaches to scaling can lead to significantly different results in the accuracy of a solution. To that end, we test seven different data transformation methods on four different temporal dimensions and study their effect on the classification accuracy of five recent methods. We show that, for the large majority of tested datasets, the best transformation-dimension configuration leads to an increase in the accuracy compared to the result of each model with the same hyperparameters and no scaling, ranging from 0.16 to 76.79 percentage points. We also show that if we keep the transformation method constant, there is a statistically significant difference in accuracy results when applying it across different dimensions, with accuracy differences ranging from 0.23 to 47.79 percentage points. Finally, we explore the relation of the transformation methods and dimensions to the classifiers, and we conclude that there is no prominent general trend, and the optimal configuration is dataset- and classifier-specific.
翻译:在目前的研究中,用于时间数据分类的机器和深层次学习解决方案正在从单通道数据集(单轨)向多个信息渠道(多轨)的问题(多轨)转变,这些作品大多侧重于方法的新颖性和结构,输入数据的格式往往被暗含处理。特别是,从输入预处理的角度看,多变数据集常常被视为一组单流时间序列,每个频道分别采用按比例尺度的方法。在本次评估中,我们的目的是证明,频道的额外层面远远不是微不足道的,而扩大范围的不同方法可能导致在解决方案的准确性方面产生显著不同的结果。为此,我们测试了四个不同时间层面的七种不同的数据转换方法,并研究了其对最近五种方法的分类准确性的影响。我们表明,就绝大多数测试过的数据集而言,最好的变异式-变异组合导致与每个模型的结果相比准确性提高准确性,从0.16到76.79个百分点不等。我们还表明,如果我们保持这一转换方法的精确度不变,那么,我们之间的精确度和精确度之间就是一个统计性差异。