Abstract
This paper focuses on explaining changes over time in globally-sourced, annual temporal data, with the specific objective of identifying pivotal factors that contribute to these temporal shifts. Leveraging such analytical frameworks can yield transformative impacts, including the informed refinement of public policy and the identification of key drivers affecting a country's economic evolution. We employ Local Interpretable Model-agnostic Explanations (LIME) to shed light on national happiness indices, economic freedom, and population metrics, spanning variable time frames. Acknowledging the presence of missing values, we employ three imputation approaches to generate robust multivariate time-series datasets apt for LIME's input requirements. Our methodology's efficacy is substantiated through a series of empirical evaluations involving multiple datasets. These evaluations include comparative analyses against random feature selection, correlation with real-world events as elucidated by LIME, and validation through Individual Conditional Expectation (ICE) plots, a state-of-the-art technique proficient in feature importance detection.
Abstract (translated)
本论文重点解释了随着全球时间数据的来源和年度变化，这些时间变化背后的关键因素。利用这样的分析框架可以产生颠覆性的影响，包括公共政策的知觉优化和影响国家经济进化的关键驱动因素的识别。我们采用局部可解释模型（LIME）来阐明国家幸福指数、经济自由度和人口指标等跨变幅的时间框架。 承认缺失值的存在，我们采用三种插值方法生成适用于LIME输入需求的稳健多维时间序列数据。我们通过多个数据集的实证评估来证明我们方法的效力。这些评估包括与随机特征选择进行比较的分析，与LIME所阐明的现实事件的相关性，以及通过个体条件期望（ICE）图进行的验证，这是一种最先进的特征重要性检测技术。
URL
https://arxiv.org/abs/2404.11874
