Interpreting Time Series Transformer Models and Sensitivity Analysis of Population Age Groups to COVID-19 Infections

TL;DR

This paper interprets transformer-based time series models for COVID-19 case prediction, evaluates feature sensitivity across age groups, and demonstrates the approach's applicability to other domains like traffic and electricity data.

Contribution

It introduces a novel interpretation framework for transformer models in time series analysis, focusing on COVID-19 data and sensitivity to population age groups.

Findings

Best transformer model achieved high prediction accuracy.

Sensitivity analysis reveals key age groups influencing infection rates.

The interpretation method is effective across different time-series datasets.

Abstract

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.