Paper ID: 2210.13358

Novelty Detection in Time Series via Weak Innovations Representation: A Deep Learning Approach

Xinyi Wang, Mei-jen Lee, Qing Zhao, Lang Tong

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.

Submitted: Oct 24, 2022