Enhanced Time Series Forecasting of Employment Ratio Using Stacked Deep Learning Models

The proposed Stacked BiLSTM model demonstrates enhanced predictive accuracy by effectively capturing both past and future temporal relationships within the sequence data. This comprehensive model provides precise and explainable results for forecasting employment trends, facilitating more informed decision-making in the development of economic and workforce policies. The performance of the model is evaluated using metrics such as an R² of 0.9801, an Mean Squared Error(MSE) of 0.0008, an Mean Absolute Error(MAE) of 0.0225, an Explained Variance Score of 0.9845, and a Pearson Correlation of 0.9924, which explicitly surpasses the performance of GRU and LSTM models. The reliability and clarity of the model are further analysed through visualisation techniques, comprising residual distribution plots and training-validation MAE curves, while SHAP and LIME analyses provide meaningful insights into feature contributions.