多变量时间序列预测中带有外生变量的反事实解释
Counterfactual Explanation for Multivariate Time Series Forecasting with Exogenous Variables
AI总结：
本研究提出了一种基于外生变量的时间序列预测反事实解释方法，通过分析变量影响并生成高质量的反事实解释，提升模型可解释性与实际应用价值。
摘要：
目前，机器学习被广泛应用于各个领域，包括时间序列数据分析。然而，某些机器学习模型充当黑箱，使可解释性成为关键问题。解决这一问题的一种方法是反事实解释（CE），其旨在提供对模型预测的见解。本研究聚焦于相对未被充分探索的问题，即生成时间序列预测的反事实解释。我们提出了一种方法，用于使用外生变量提取CEs，这些变量在商业和营销等领域经常出现。此外，我们还提出了分析每个变量对整个时间序列影响的方法，通过仅改变特定变量生成CEs，并评估所生成CEs的质量。我们通过理论分析和实证实验验证了所提出的方法，展示了其准确性和实际应用价值。这些贡献预计将支持基于时间序列数据分析的现实决策。
英文摘要：
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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