Deep Sequential Learning with Adaptive Sampling for Macro-Financial Yield Curve Prediction

This paper develops a deep supervised learning framework for sequential prediction of macro-financial time series derived from the U.S. Treasury yield curve. Using daily data on the 10-year Treasury constant maturity rate and the 3-month Treasury bill rate from the Federal Reserve Economic Data (FRED) system, we construct a sequence-based representation that captures level effects, yield spreads, short-term dynamics, and local volatility. We formulate the problem as a supervised learning task for predicting one-step-ahead changes in the 10-year Treasury yield. To model temporal dependence, we employ a hybrid convolutional–recurrent neural network that integrates convolutional layers for local pattern extraction and LSTM layers for capturing longer-range temporal structure. To improve training efficiency and robustness under nonstationary financial conditions, we investigate three experience replay strategies: uniform sampling, entropy–variance-based sampling that incorporates predictive uncertainty, and prediction-error-based sampling that prioritizes high-residual observations. Empirical results show that the choice of sampling strategy significantly affects both learning stability and out-of-sample predictive performance. Uniform sampling yields the most stable and competitive performance. Entropy-based sampling achieves strong late-stage predictive accuracy, indicating the benefit of uncertainty-aware data selection. In contrast, prediction-error-based sampling accelerates early learning but exhibits higher variance and reduced stability under structural breaks and regime shifts in macro-financial dynamics. Overall, the findings highlight a trade-off between stability, convergence behavior, and sensitivity to informative but potentially noisy observations in sequential financial prediction problems. The results suggest that simpler sampling strategies can be highly competitive in macro-financial environments characterized by nonstationarity and heteroskedasticity, while uncertainty-aware sampling provides a promising direction for improving predictive performance under uncertainty.