Interpreting Time Series Forecasts with LIME and SHAP: A Case Study on the Air Passengers Dataset
用 LIME 与 SHAP 解释时间序列预测:航空旅客数据案例
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)…