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：多元时间序列分类是一项重要的计算任务，出现在需要记录随时间变化和多通道数据的应用中。例如，智能手表可以记录人体运动的加速度和方向，这些信号被记录为多元时间序列。我们可以对这些数据进行分类，以理解和预测人类运动及诸如体能水平等属性。在许多应用中，仅进行分类是不够的，我们通常还需要分类并理解模型学到了什么（例如，基于数据中的哪些信息给出了某个预测）。本文的主要焦点是分析和评估专门针对多元时间序列分类（MTSC）的解释方法。我们关注基于显著性的解释方法，这些方法能够指出对分类决策最相关的通道和时间序列点。我们分析了两种流行且准确的多元时间序列分类器——ROCKET和dResNet，以及两种流行的解释方法——SHAP和dCAM。我们在3个合成数据集和2个真实世界数据集上研究了这些方法，并对所提供的解释进行了定量和定性分析。我们发现，通过拼接通道来展平多元数据集的效果与直接使用多元分类器相当，并且针对MTSC调整的SHAP方法表现良好。此外，我们还发现所使用的流行合成数据集并不适用于时间序列分析。