Time-series forecasting is widely applied across various domains, yet most approaches rely on predefined time steps given by each problem. Based on observations from dynamic systems with known ground truth, we identify that large-step forecasts can lead to substantial errors due to insufficient modeling of continuous dynamics. To address this, we propose a micro-step time-series regression technique that decomposes predictions into smaller intervals, so that genetic programming-based feature construction can capture finer temporal patterns to improve the prediction performance. Specifically, we employ linear interpolation to allow the evolutionary feature construction process to learn from incremental changes, reducing the difficulty of time-series regression. Experiments on 100 datasets from the M4 forecasting benchmark demonstrate that micro-step regression significantly enhances prediction accuracy compared to traditional methods using raw time steps. Further analysis reveals that features trained on micro-step data evolve into simpler structures, promoting both generalization and interpretability.