We develop and apply a new online early warning system (EWS) for what is known in machine learning as concept drift, in economics as a regime shift and in statistics as a change point. The system goes beyond linearity assumed in many conventional methods, and is robust to heavy tails and tail-dependence in the data, making it particularly suitable for emerging markets. The key component is an effective change-point detection mechanism for conditional entropy of the data, rather than for a particular indicator of interest. Combined with recent advances in machine learning methods for high-dimensional random forests, the mechanism is capable of finding significant shifts in information transfer between interdependent time series when traditional methods fail. We explore when this happens using simulations and we provide illustrations by applying the method to Uzbekistan's commodity and equity markets as well as to Russia's equity market in 2021-2023.

翻译：我们开发并应用了一种新型在线早期预警系统（EWS），用于识别机器学习中称为概念漂移、经济学中称为体制转换、统计学中称为变点的现象。该系统突破了传统方法中常见的线性假设假设，对数据中的厚尾分布和尾部依赖性具有鲁棒性，使其特别适用于新兴市场。其核心组件并非针对特定兴趣指标，而是针对数据的条件熵建立有效的变点检测机制。结合高维随机森林机器学习方法的最新进展，该机制能够在传统方法失效时，发现相互依赖时间序列之间信息传递的显著变化。我们通过模拟实验探究了该机制的生效条件，并利用乌兹别克斯坦商品及股票市场以及2021-2023年俄罗斯股票市场的实际案例进行了应用验证。