Using a Local Surrogate Model to Interpret Temporal Shifts in Global
  Annual Data
Shou Nakano Yang Liu
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.