Multivariate time series classification is an important computational task arising in applications where data is recorded over time and over multiple channels. For example, a smartwatch can record the acceleration and orientation of a person's motion, and these signals are recorded as multivariate time series. We can classify this data to understand and predict human movement and various properties such as fitness levels. In many applications classification alone is not enough, we often need to classify but also understand what the model learns (e.g., why was a prediction given, based on what information in the data). The main focus of this paper is on analysing and evaluating explanation methods tailored to Multivariate Time Series Classification (MTSC). We focus on saliency-based explanation methods that can point out the most relevant channels and time series points for the classification decision. We analyse two popular and accurate multivariate time series classifiers, ROCKET and dResNet, as well as two popular explanation methods, SHAP and dCAM. We study these methods on 3 synthetic datasets and 2 real-world datasets and provide a quantitative and qualitative analysis of the explanations provided. We find that flattening the multivariate datasets by concatenating the channels works as well as using multivariate classifiers directly and adaptations of SHAP for MTSC work quite well. Additionally, we also find that the popular synthetic datasets we used are not suitable for time series analysis.

翻译：多变量时间序列分类是一项重要的计算任务，广泛应用于数据随时间跨多个通道记录的场景。例如，智能手表可记录人体运动的加速度与方向，这些信号构成多变量时间序列。通过对这些数据进行分类，我们能够理解和预测人体运动及体能水平等特性。然而在许多应用中，仅实现分类功能并不足够，我们往往需要在分类的同时理解模型的学习机制（例如，预测结果的依据，以及具体依赖数据中的哪些信息）。本文的核心在于分析与评估专为多变量时间序列分类（MTSC）设计的解释方法。我们聚焦于基于显著性的解释方法，这类方法能够识别出对分类决策最关键的通道和时间序列点。研究选取了两种流行且准确的多变量时间序列分类器（ROCKET和dResNet）以及两种主流解释方法（SHAP和dCAM），在3个合成数据集和2个真实数据集上展开实验，并对生成的解释结果进行定量与定性分析。研究发现，通过拼接通道将多变量数据集展平的处理方式，其效果与直接使用多变量分类器相当；同时，针对MTSC改进的SHAP方法表现良好。此外，我们还发现研究中常用的合成数据集并不适用于时间序列分析。