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

• URL: http://arxiv.org/abs/2508.12253v1
• Date: Sun, 17 Aug 2025 06:22:29 GMT
• Title: Interpreting Time Series Forecasts with LIME and SHAP: A Case Study on the Air Passengers Dataset
• Authors: Manish Shukla,
• Abstract summary: Time-series forecasting underpins critical decisions across aviation, energy, retail and health.<n>This paper presents a unified framework for interpreting time-series forecasts using local interpretable model-agnostic explanations.<n>We show that a small set of lagged features -- particularly the twelve-month lag -- and seasonal encodings explain most forecast variance.
• Score: 1.9943074894669668
• License: http://creativecommons.org/licenses/by/4.0/
• 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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