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

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.