A perturbative approach to novelty detection in autoregressive models

Maurizio Filippone*, Guido Sanguinetti

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


We propose a new method to perform novelty detection in dynamical systems governed by linear autoregressive models. The method is based on a perturbative expansion to a statistical test whose leading term is the classical F-test, and whose O(1/n) correction can be approximated as a function of the number of training points and the model order alone. The method can be justified as an approximation to an information theoretic test. We demonstrate on several synthetic examples that the first correction to the F-test can dramatically improve the control over the false positive rate of the system. We also test the approach on some real time series data, demonstrating that the method still retains a good accuracy in detecting novelties.

Original languageEnglish (US)
Article number5676236
Pages (from-to)1027-1036
Number of pages10
JournalIEEE Transactions on Signal Processing
Issue number3
StatePublished - Mar 2011


  • autoregressive modeling
  • novelty detection
  • statistical testing
  • Time series

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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