论文标题
时间序列中的新颖性检测通过薄弱的创新表示：一种深度学习方法
Novelty Detection in Time Series via Weak Innovations Representation: A Deep Learning Approach
论文作者
论文摘要
我们考虑具有未知和非参数概率结构的时间序列中的新颖性检测。提出了一种深度学习方法，以提取由统计学上独立于过去所有样本的新颖性样本组成的创新序列。开发了一种新颖的检测算法，用于在线检测创新序列中概率结构的新变化。为提出的新颖性检测方法建立了贝叶斯风险度量下的最小值最佳性，并在使用真实和合成数据集的实验中证明了其鲁棒性和功效。
We consider novelty detection in time series with unknown and nonparametric probability structures. A deep learning approach is proposed to causally extract an innovations sequence consisting of novelty samples statistically independent of all past samples of the time series. A novelty detection algorithm is developed for the online detection of novel changes in the probability structure in the innovations sequence. A minimax optimality under a Bayes risk measure is established for the proposed novelty detection method, and its robustness and efficacy are demonstrated in experiments using real and synthetic datasets.
