Interpreting deep learning time series models is crucial in understanding the
model's behavior and learning patterns from raw data for real-time
decision-making. However, the complexity inherent in transformer-based time
series models poses challenges in explaining the impact of individual features
on predictions. In this study, we leverage recent local interpretation methods
to interpret state-of-the-art time series models. To use real-world datasets,
we collected three years of daily case data for 3,142 US counties. Firstly, we
compare six transformer-based models and choose the best prediction model for
COVID-19 infection. Using 13 input features from the last two weeks, we can
predict the cases for the next two weeks. Secondly, we present an innovative
way to evaluate the prediction sensitivity to 8 population age groups over
highly dynamic multivariate infection data. Thirdly, we compare our proposed
perturbation-based interpretation method with related work, including a total
of eight local interpretation methods. Finally, we apply our framework to
traffic and electricity datasets, demonstrating that our approach is generic
and can be applied to other time-series domains.

通过对深度学习时间序列模型进行解释，我们可以了解模型的行为和从原始数据中学习到的模式，以便进行实时决策。本研究使用最新的局部解释方法解释了最先进的时间序列模型，并通过收集三年的美国县级每日病例数据来验证了该方法的可行性。我们利用 13 个输入特征和过去两周的数据来预测未来两周的 COVID-19 感染情况，提出了一种对人口年龄组高度动态多变的感染数据进行预测敏感性评估的创新方法，并将我们的框架应用于交通和电力数据集，证明了该方法的通用性。