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：在当前研究中，针对时序数据分类的机器学习与深度学习方法正从单通道数据集（单变量）向多通道信息问题（多变量）转变。多数研究聚焦于方法创新与架构设计，而输入数据的格式往往被隐式处理。特别是在输入预处理阶段，多变量数据集常被视为单变量时间序列的堆叠，并在各通道上分别应用缩放方法。本评估旨在证明额外的通道维度并非微不足道，不同缩放方法可能导致解决方案准确率的显著差异。为此，我们在四个不同时间维度上测试了七种数据变换方法，研究其对五种最新方法分类准确率的影响。结果表明，对于绝大多数测试数据集，相较于采用相同超参数但无缩放处理的各模型结果，最佳变换-维度配置可使准确率提升0.16至76.79个百分点。同时发现，若固定变换方法不变，在不同维度上应用该方法时准确率存在统计显著差异，差异幅度为0.23至47.79个百分点。最后，我们探究了变换方法与维度同分类器之间的关联，得出当前不存在显著普遍趋势的结论，最优配置因数据集和分类器而异。