Interpreting Temporal Shifts in Global Annual Data Using Local Surrogate Models

Shou Nakano and Yang Liu ()
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Shou Nakano: Physics and Computer Science, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada
Yang Liu: Physics and Computer Science, Wilfrid Laurier University, Waterloo, ON N2L 3C5, Canada

Mathematics, 2025, vol. 13, issue 4, 1-14

Abstract: This paper focuses on explaining changes over time in globally sourced annual temporal data with the specific objective of identifying features in black-box models that contribute to these temporal shifts. Leveraging local explanations, a part of explainable machine learning/XAI, can yield explanations behind a country’s growth or downfall after making economic or social decisions. We employ a Local Interpretable Model-Agnostic Explanation (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 temporal 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 explained using LIME, and validation through Individual Conditional Expectation (ICE) plots, a state-of-the-art technique proficient in feature importance detection.

Keywords: artificial intelligence; FLAML; imputation; Local Interpretable Model-Agnostic Explanation; machine learning; XAI (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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