{"ID":2881373,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.12253","arxiv_id":"2508.12253","title":"Interpreting Time Series Forecasts with LIME and SHAP: A Case Study on the Air Passengers Dataset","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.","short_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 bu...","url_abs":"https://arxiv.org/abs/2508.12253","url_pdf":"https://arxiv.org/pdf/2508.12253v1","authors":"[\"Manish Shukla\"]","published":"2025-08-17T06:22:29Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\",\"stat.ME\"]","methods":"[]","has_code":false}