标题： 时间序列特征冗余悖论：基于抵押违约预测的实证研究 显示英文标题

标题： Time Series Feature Redundancy Paradox: An Empirical Study Based on Mortgage Default Prediction

摘要： 随着机器学习在金融风险管理中的广泛应用，传统观点认为，更长的训练周期和更多的特征变量有助于提升模型性能。 本文聚焦于抵押贷款违约预测，实证发现了一种与传统认知相悖的现象：在时间序列预测中，增加训练数据的时间跨度和额外的非关键特征实际上会导致预测效果显著恶化。 利用房利美（Fannie Mae）的抵押贷款数据，研究通过比较不同时间窗口长度（如2012-2022年）以及特征组合下的预测表现，揭示出较短的时间窗口（如单一年度期间）搭配精心挑选的关键特征能够获得更优的预测结果。 实验结果表明，延长的时间跨度可能会引入历史数据中的噪声及过时的市场模式，而过多的非关键特征会干扰模型对核心违约因素的学习。 这项研究不仅挑战了数据建模中传统的“越多越好”观念，还为金融风险预测中的特征选择与时间窗口优化提供了新的见解和实用指导。 显示英文摘要

摘要： With the widespread application of machine learning in financial risk management, conventional wisdom suggests that longer training periods and more feature variables contribute to improved model performance. This paper, focusing on mortgage default prediction, empirically discovers a phenomenon that contradicts traditional knowledge: in time series prediction, increased training data timespan and additional non-critical features actually lead to significant deterioration in prediction effectiveness. Using Fannie Mae's mortgage data, the study compares predictive performance across different time window lengths (2012-2022) and feature combinations, revealing that shorter time windows (such as single-year periods) paired with carefully selected key features yield superior prediction results. The experimental results indicate that extended time spans may introduce noise from historical data and outdated market patterns, while excessive non-critical features interfere with the model's learning of core default factors. This research not only challenges the traditional "more is better" approach in data modeling but also provides new insights and practical guidance for feature selection and time window optimization in financial risk prediction.