用LIME和SHAP解释时间序列预测，揭示关键滞后特征
Interpreting Time Series Forecasts with LIME and SHAP: A Case Study on the Air Passengers Dataset
- 将时序转为无泄漏的监督学习问题，用树模型与ARIMA对比
- 十二个月滞后项和季节编码解释了大部分预测变化
- 适合需要可解释性的金融、交通等领域从业者
时间序列预测支撑航空、能源、零售和医疗等领域的关键决策。传统自回归积分滑动平均（ARIMA）模型通过系数具备可解释性，但难以处理非线性；而树模型如XGBoost精度高，却常为黑箱。本文提出统一框架，结合局部可解释模型无关解释（LIME）与Shapley加性解释（SHAP），将单变量时序转化为无泄漏的监督学习任务，训练梯度提升树模型并对比ARIMA基线，进行后验解释。以航空乘客数据集为例，结果表明少量滞后特征——尤其是十二个月滞后项——以及季节编码，解释了大部分预测方差。本文贡献包括：(i) 一种不破坏时间顺序的时间序列解释方法；(ii) 算法原理的理论阐述；(iii) 详尽的实证评估；(iv) 实践指导建议。
原文摘要 · Abstract (English)
Time-series forecasting underpins critical decisions across aviation, energy, retail and health. Classical autoregressive integrated moving average (ARIMA) models offer interpretability via coefficients but struggle with nonlinearities, whereas tree-based machine-learning models such as XGBoost deliver high accuracy but are often opaque. This paper presents a unified framework for interpreting time-series forecasts using local interpretable model-agnostic explanations (LIME) and SHapley additive exPlanations (SHAP). We convert a univariate series into a leakage-free supervised learning problem, train a gradient-boosted tree alongside an ARIMA baseline and apply post-hoc explainability. Using the Air Passengers dataset as a case study, we show that a small set of lagged features -- particularly the twelve-month lag -- and seasonal encodings explain most forecast variance. We contribute: (i) a methodology for applying LIME and SHAP to time series without violating chronology; (ii) theoretical exposition of the underlying algorithms; (iii) empirical evaluation with extensive analysis; and (iv) guidelines for practitioners.
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