非平稳时间序列外生异常识别的Koopman理论方法
Koopman-theoretic Approach for Identification of Exogenous Anomalies in Nonstationary Time-series Data
摘要：
在许多情况下，有必要通过时间序列观测来监测复杂系统，并确定异常外源事件何时发生，以便采取相关行动。确定当前观察结果是否异常具有挑战性。它需要从历史数据中学习动态的外推概率模型，并使用有限数量的当前观测进行分类。我们利用长期概率预测的最新进展，即{ \em Deep ProbabilityKoopman}，建立一个通用的方法来分类多维时间序列数据中的异常。我们还展示了如何利用模型与域知识的动态，以减少I型和II型错误。我们证明了我们提出的方法对全球大气污染监测的重要现实世界的任务，将其与美国宇航局的全球地球系统模型。该系统成功检测到因COVID-19封锁和野火等事件而导致的局部空气质量异常。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
