论文标题
Koopman理论方法用于鉴定非组织时间序列数据中的外源异常
Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data
论文作者
论文摘要
在许多情况下，有必要通过观测时间序列监视复杂的系统，并确定何时发生异源事件，以便可以采取相关的动作。确定当前的观察是否异常是具有挑战性的。它需要从历史数据中学习动力学的外推性概率模型，并使用有限数量的当前观察结果来进行分类。我们利用长期概率预测的最新进展，即{\ em Deep概率的Koopman}，构建了一种在多维时序列数据中对异常进行分类的通用方法。我们还展示了如何利用具有域知识的模型来减少I型和II型错误。我们展示了我们提出的关于全球大气污染监测的重要现实世界任务的方法，并将其与NASA的全球地球系统模型集成在一起。该系统成功地检测到由于COVID-19锁定和野火等事件而导致的空气质量局部异常。
In many scenarios, it is necessary to monitor a complex system via a time-series of observations and determine when anomalous exogenous events have occurred so that relevant actions can be taken. Determining whether current observations are abnormal is challenging. It requires learning an extrapolative probabilistic model of the dynamics from historical data, and using a limited number of current observations to make a classification. We leverage recent advances in long-term probabilistic forecasting, namely {\em Deep Probabilistic Koopman}, to build a general method for classifying anomalies in multi-dimensional time-series data. We also show how to utilize models with domain knowledge of the dynamics to reduce type I and type II error. We demonstrate our proposed method on the important real-world task of global atmospheric pollution monitoring, integrating it with NASA's Global Earth System Model. The system successfully detects localized anomalies in air quality due to events such as COVID-19 lockdowns and wildfires.
