Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Using a Local Surrogate Model to Interpret Temporal Shifts in Global Annual Data

Shou Nakano, Yang Liu

TL;DR

This work tackles the challenge of explaining temporal shifts in globally sourced annual data by applying Local Interpretable Model-agnostic Explanations (LIME) to multivariate time-series across countries. It integrates multiple imputation strategies, FLAML-based model training, and ICE validation to produce interpretable explanations for happiness, economic freedom, and population dynamics. Through three open datasets and case studies (notably Syria and Brazil), the approach demonstrates that LIME can identify influential drivers and that its explanations align with real-world events, offering potential policy and economic planning insights. The study highlights the practical viability of combining local surrogates with automated model selection to make complex temporal predictions more transparent and actionable.

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.

Using a Local Surrogate Model to Interpret Temporal Shifts in Global Annual Data

TL;DR

This work tackles the challenge of explaining temporal shifts in globally sourced annual data by applying Local Interpretable Model-agnostic Explanations (LIME) to multivariate time-series across countries. It integrates multiple imputation strategies, FLAML-based model training, and ICE validation to produce interpretable explanations for happiness, economic freedom, and population dynamics. Through three open datasets and case studies (notably Syria and Brazil), the approach demonstrates that LIME can identify influential drivers and that its explanations align with real-world events, offering potential policy and economic planning insights. The study highlights the practical viability of combining local surrogates with automated model selection to make complex temporal predictions more transparent and actionable.

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.
Paper Structure (15 sections, 4 equations, 5 figures, 5 tables, 1 algorithm)

This paper contains 15 sections, 4 equations, 5 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Methodology
  4. Problem Definition
  5. Data Collection
  6. Preprocessing
  7. Building Models with FLAML
  8. Interpreting with LIME
  9. Results and Discussions
  10. Experiment Setting
  11. LIME's Results
  12. Case Studies
  13. Comparison with State-of-the-art Methods
  14. Conclusion
  15. Dataset Snippets

Figures (5)

  • Figure 1: Predictions by different models for Dataset 2 and Dataset 3
  • Figure 2: A bar graph comparing the $R^2$ values of selecting random columns VS selecting columns with LIME.
  • Figure 3: A prediction made by LIME showing the columns that most contributed to Syria's drop in Economic Freedom between 2011 and 2012.
  • Figure 4: A prediction made by LIME showing the columns that most contributed to the rise in Brazil's Economic Freedom from 1995 to 2000.
  • Figure 5: ICE Plots showing how greatly the columns that are most selected by LIME affect the change in population, for Dataset 3.