为带外部变量的时间序列预测提供可解释的反事实分析方法
Counterfactual Explanation for Multivariate Time Series Forecasting with Exogenous Variables
- 基于外部变量生成反事实解释，揭示预测变化的关键驱动因素
- 可定位特定变量变化对整个时序预测的影响，支持精准干预决策
- 适用于商业、营销等依赖时间序列的现实场景，提升模型可信度
当前机器学习广泛应用于各领域，包括时间序列数据分析。然而，部分模型呈现黑箱特性，可解释性成为关键挑战。反事实解释（CE）是一种提升模型透明度的有效手段。本文聚焦于时间序列预测中反事实解释这一相对未被充分探索的问题，提出一种利用外部变量生成反事实解释的方法，该类变量在商业与营销等领域常见。我们进一步提出分析各变量在整个时间序列上影响的方法，仅改变特定变量即可生成反事实解释，并评估其质量。通过理论分析与实证实验验证了所提方法的准确性与实用性，有望支持基于时间序列数据的真实决策。
原文摘要 · Abstract (English)
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.
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