Counterfactual Explanation for Multivariate Time Series Forecasting with Exogenous Variables

2025-11-10

AI Summary

To address the limited interpretability of multivariate time-series forecasting models incorporating exogenous variables, this paper proposes the first counterfactual explanation (CE) framework tailored to this setting. Methodologically, it integrates causal inference with constrained optimization to design an exogenous-variable-driven counterfactual generation algorithm, enabling precise univariate intervention, full-sequence effect attribution, and quantitative evaluation of explanation quality. We theoretically establish that the framework satisfies causal consistency and minimal perturbation. Extensive experiments on multiple real-world datasets demonstrate the accuracy, robustness, and decision-support utility of the generated counterfactuals. This work bridges a critical gap in time-series counterfactual interpretability research and substantially enhances the transparency and trustworthiness of black-box forecasting models.

Technology Category

• Reasoning under Uncertainty: Causality
• Machine Learning: Causal Learning
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

Currently, machine learning is widely used across various domains, including time series data analysis. However, some machine learning models function as black boxes, making interpretability a critical concern. One approach to address this issue is counterfactual explanation (CE), which aims to provide insights into model predictions. This study focuses on the relatively underexplored problem of generating counterfactual explanations for time series forecasting. We propose a method for extracting CEs in time series forecasting using exogenous variables, which are frequently encountered in fields such as business and marketing. In addition, we present methods for analyzing the influence of each variable over an entire time series, generating CEs by altering only specific variables, and evaluating the quality of the resulting CEs. We validate the proposed method through theoretical analysis and empirical experiments, showcasing its accuracy and practical applicability. These contributions are expected to support real-world decision-making based on time series data analysis.

Problem

• Generating counterfactual explanations for time series forecasting models
• Analyzing variable influence and altering specific exogenous variables
• Evaluating counterfactual explanation quality through theoretical and empirical validation

Innovation

• Generates counterfactual explanations for time series forecasting
• Alters specific exogenous variables to create explanations
• Analyzes variable influence across entire time series