用合成异常数据提升金融模型在动荡经济中的稳定性
Mitigating Model Drift in Developing Economies Using Synthetic Data and Outliers
- 通过生成少量合成异常数据增强模型抗干扰能力
- 实验显示该方法可显著降低模型性能下降幅度
- 特别适合应对中亚、高加索地区突发经济冲击的场景
金融领域的机器学习模型极易受模型漂移影响，导致预测性能随数据分布变化而下降。这一问题在中亚和高加索地区（如塔吉克斯坦、乌兹别克斯坦、哈萨克斯坦、阿塞拜疆）尤为突出，因频繁且不可预测的宏观经济冲击使金融数据极不稳定。据我们所知，这是首个针对这些地区金融数据集研究漂移缓解方法的论文。本文探索了一种尚未被充分研究的方法——合成异常数据，以提高模型对意外冲击的鲁棒性。为评估效果，提出一个两级评估框架，分别衡量性能退化程度与冲击严重性。在宏观经济表格数据集上的实验表明，添加少量合成异常数据通常能提升模型稳定性，但最优比例因数据集和模型而异。
原文摘要 · Abstract (English)
Machine Learning models in finance are highly susceptible to model drift, where predictive performance declines as data distributions shift. This issue is especially acute in developing economies such as those in Central Asia and the Caucasus - including Tajikistan, Uzbekistan, Kazakhstan, and Azerbaijan - where frequent and unpredictable macroeconomics shocks destabilize financial data. To the best of our knowledge, this is among the first studies to examine drift mitigation methods on financial datasets from these regions. We investigate the use of synthetic outliers, a largely unexplored approach, to improve model stability against unforeseen shocks. To evaluate effectiveness, we introduce a two-level framework that measures both the extent of performance degradation and the severity of shocks. Our experiments on macroeconomic tabular datasets show that adding a small proportion of synthetic outliers generally improves stability compared to baseline models, though the optimal amount varies by dataset and model
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