TSFeatLIME提升单变量时间序列预测可解释性，让非计算机背景用户更易理解模型决策。
TSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecasting
- 在代理模型中引入辅助特征并考虑时间序列间距离，提升解释保真度。
- 实验表明解释效果显著优于基线，且不影响预测精度。
- 对非计算机背景用户尤其有效，适合实际应用中的可解释性需求。
时间序列预测在诸多应用中至关重要，但复杂模型常难以被人类理解。有效的可解释AI技术对于弥合模型预测与用户认知之间的差距至关重要。本文提出一种新框架TSFeatLIME，基于TSLIME，专为单变量时间序列预测的解释设计。该框架在代理模型中引入辅助特征，并考虑查询时间序列与生成样本间的成对欧氏距离，以提升代理模型的保真度。然而，此类解释对人类是否真正有用仍是开放问题。为此，我们通过两种交互界面开展了包含160名参与者在线用户研究，比较不同背景用户在处理组与对照组中模拟或预测模型输出变化的能力。结果表明，TSFeatLIME框架下的代理模型在保持准确性的前提下，能更精准地模拟黑盒行为；此外，用户研究表明，解释对无计算机科学背景的参与者更为有效。
原文摘要 · Abstract (English)
Time series forecasting, while vital in various applications, often employs complex models that are difficult for humans to understand. Effective explainable AI techniques are crucial to bridging the gap between model predictions and user understanding. This paper presents a framework - TSFeatLIME, extending TSLIME, tailored specifically for explaining univariate time series forecasting. TSFeatLIME integrates an auxiliary feature into the surrogate model and considers the pairwise Euclidean distances between the queried time series and the generated samples to improve the fidelity of the surrogate models. However, the usefulness of such explanations for human beings remains an open question. We address this by conducting a user study with 160 participants through two interactive interfaces, aiming to measure how individuals from different backgrounds can simulate or predict model output changes in the treatment group and control group. Our results show that the surrogate model under the TSFeatLIME framework is able to better simulate the behaviour of the black-box considering distance, without sacrificing accuracy. In addition, the user study suggests that the explanations were significantly more effective for participants without a computer science background.
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