# Improving accuracy in short mortality rate series: Exploring Multi-step Forecasting Approaches in Hybrid Systems

arXiv: https://arxiv.org/abs/2509.22395 · HTML: https://mlsanity.com/paper/2509.22395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 35 references that are in the map.
- First posted on arXiv 26 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [An Experimental Review on Deep Learning Architectures for Time Series Forecasting](https://mlsanity.com/paper/2103.12057/) (2021)
- [Evaluation of deep learning models for multi-step ahead time series prediction](https://mlsanity.com/paper/2103.14250/) (2021)

## Abstract (arXiv)

The decline in interest rates and economic stabilization has heightened the importance of accurate mortality rate forecasting, particularly in insurance and pension markets. Multi-step-ahead predictions are crucial for public health, demographic planning, and insurance risk assessments; however, they face challenges when data are limited. Hybrid systems that combine statistical and Machine Learning (ML) models offer a promising solution for handling both linear and nonlinear patterns. This study evaluated the impact of different multi-step forecasting approaches (Recursive, Direct, and Multi-Input Multi-Output) and ML models on the accuracy of hybrid systems. Results from 12 datasets and 21 models show that the selection of both the multi-step approach and the ML model is essential for improving performance, with the ARIMA-LSTM hybrid using a recursive approach outperforming other models in most cases.