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

TL;DR

This paper introduces a unified approach using LIME and SHAP to interpret time series forecasts, demonstrating its effectiveness on the Air Passengers dataset and providing practical guidelines.

Contribution

It presents a novel methodology for applying local explainability methods to time series forecasts without data leakage, along with theoretical insights and practical recommendations.

Findings

A small set of lagged features explains most forecast variance.

The 12-month lag is particularly influential in the model.

The approach maintains temporal integrity while providing interpretability.

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.