Change detection is of fundamental importance when analyzing data streams. Detecting changes both quickly and accurately enables monitoring and prediction systems to react, e.g., by issuing an alarm or by updating a learning algorithm. However, detecting changes is challenging when observations are high-dimensional. In high-dimensional data, change detectors should not only be able to identify when changes happen, but also in which subspace they occur. Ideally, one should also quantify how severe they are. Our approach, ABCD, has these properties. ABCD learns an encoder-decoder model and monitors its accuracy over a window of adaptive size. ABCD derives a change score based on Bernstein's inequality to detect deviations in terms of accuracy, which indicate changes. Our experiments demonstrate that ABCD outperforms its best competitor by at least 8% and up to 23% in F1-score on average. It can also accurately estimate changes' subspace, together with a severity measure that correlates with the ground truth.

翻译：变化检测是分析数据流时至关重要的任务。快速而准确地检测变化能使监测与预测系统做出反应，例如发出警报或更新学习算法。然而，当观测数据为高维时，变化检测面临挑战。在高维数据中，变化检测器不仅需要识别变化发生的时间，还需确定其发生的子空间。理想情况下，还应量化变化的严重程度。我们的方法ABCD具备这些特性。ABCD学习一个编码器-解码器模型，并监测其在自适应大小窗口内的精度。该方法基于伯恩斯坦不等式推导出变化分数，用于检测精度偏差，从而指示变化。实验表明，在F1分数上，ABCD平均比最优竞争者高出8%至23%。它还能准确估计变化的子空间，并提供与真实值相关的严重性度量。