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arXiv · 2508.12253

Interpreting Time Series Forecasts with LIME and SHAP: A Case Study on the Air Passengers Dataset

Manish Shukla
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Abstract

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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Manish Shukla. 2025-08-17. Interpreting Time Series Forecasts with LIME and SHAP: A Case Study on the Air Passengers Dataset. https://doi.org/10.21203/rs.3.rs-7358158%2Fv1

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