Inception Residual RNN-LSTM Hybrid Model for Predicting Pension Coverage Trends Among Private-Sector Workers in the USA

Authors

  • Kaixian Xu

    Risk & Quant Analytics, BlackRock, Jersey City, NJ 07310, USA
  • Yiqun Cai

    University of Florida, Herbert Wertheim College, Gainesville, FL 32611, USA
  • Alan Wilson

    Intact Financial Corporation, Toronto, ON M5G 0A1, Canada

DOI:

https://doi.org/10.30564/aia.v7i1.8704
Received: 5 January 2025 | Revised: 15 February 2025 | Accepted: 19 February 2025 | Published Online: 28 February 2025

Abstract

Pensions are fundamental to financial security in retirement, especially in the U.S., where they play a critical role in ensuring stability for retirees and fostering broader economic benefits. However, predicting pension coverage trends poses significant challenges due to the complexity of labor markets, demographic shifts, and economic variabilities. Traditional statistical models, though foundational, often fail to handle the nonlinear patterns inherent in pension data. To address these limitations, we propose the Inception residual RNN-LSTM hybrid model, which combines the strengths of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks with residual connections. This model captures diverse temporal patterns while mitigating vanishing gradient issues, delivering superior performance in predicting pension coverage trends. Experimental results demonstrate that our model outperforms traditional machine learning models and standalone deep learning architectures like RNN and LSTM. Its robust performance across key metrics highlights its potential as a reliable tool for forecasting complex pension trends and aiding policymakers, employers, and financial institutions in effective retirement planning.

Keywords:

Pension Coverage Prediction; Machine Learning; RNN-LSTM

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How to Cite

Xu, K., Cai, Y., & Wilson, A. (2025). Inception Residual RNN-LSTM Hybrid Model for Predicting Pension Coverage Trends Among Private-Sector Workers in the USA. Artificial Intelligence Advances, 7(1), 1–9. https://doi.org/10.30564/aia.v7i1.8704

Issue

Vol. 7 , Iss. 1 (March 2025): In progress

Article Type

Article

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Copyright © 2025 Kaixian Xu, Yiqun Cai, Alan Wilson

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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