Forecasting mortality rates with functional signatures

TL;DR

This paper presents the HUts model, integrating signature-based methods into the Hyndman-Ullah framework, to improve mortality rate forecasting accuracy and robustness across diverse demographic data.

Contribution

The paper introduces the HUts model, a novel approach combining signature regression with the HU model for enhanced mortality forecasting accuracy.

Findings

HUts outperforms traditional HU models in forecast accuracy

The model is robust against data irregularities and outliers

Prediction intervals are effectively constructed with bootstrapping

Abstract

This study introduces an innovative methodology for mortality forecasting, which integrates signature-based methods within the functional data framework of the Hyndman-Ullah (HU) model. This new approach, termed the Hyndman-Ullah with truncated signatures (HUts) model, aims to enhance the accuracy and robustness of mortality predictions. By utilizing signature regression, the HUts model is able to capture complex, nonlinear dependencies in mortality data which enhances forecasting accuracy across various demographic conditions. The model is applied to mortality data from 12 countries, comparing its forecasting performance against variants of the HU models across multiple forecast horizons. Our findings indicate that overall the HUts model not only provides more precise point forecasts but also shows robustness against data irregularities, such as those observed in countries with historical outliers. The integration of signature-based methods enables the HUts model to capture complex patterns in mortality data, making it a powerful tool for actuaries and demographers. Prediction intervals are also constructed with bootstrapping methods