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arXiv · 2210.13358

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

Xinyi Wang·Mei-jen Lee·Qing Zhao·Lang Tong
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Abstract

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.

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Xinyi Wang, Mei-jen Lee, Qing Zhao, Lang Tong. 2022-10-24. Novelty Detection in Time Series via Weak Innovations Representation: A Deep Learning Approach. https://arxiv.org/abs/2210.13358

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