Enhancing Mortality Forecasting with Ensemble Learning: A Shapley-Based Approach

TL;DR

This paper introduces a novel ensemble method for mortality forecasting that uses Shapley values to weight models age-by-age, improving accuracy and interpretability by excluding models with minimal contributions.

Contribution

It presents a Shapley value-based ensemble approach that dynamically weights models at each age and filters out negligible contributors, enhancing forecast performance.

Findings

SHAP ensemble improves out-of-sample accuracy

Method reduces forecast variance by excluding weak models

Approach is effective across 24 OECD countries

Abstract

A well-established insight in mortality forecasting is that combining predictions from a set of models improves accuracy compared to relying on a single best model. This paper proposes a novel ensemble approach based on Shapley values, a game-theoretic measure of each model's marginal contribution to the forecast. We further compute these SHapley Additive exPlanations (SHAP)-based weights age-by-age, thereby capturing the specific contribution of each model at each age. In addition, we introduce a threshold mechanism that excludes models with negligible contributions, effectively reducing the forecast variance. Using data from 24 OECD countries, we demonstrate that our SHAP ensemble enhances out-of-sample forecasting performance, especially at longer horizons. By leveraging the complementary strengths of different mortality models and filtering out those that add little predictive power, our approach offers a robust and interpretable solution for improving mortality forecasts.