  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.
