A Perturbative Approach to Novelty Detection in Autoregressive Models

Maurizio Filippone, Guido Sanguinetti

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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
Pages (from-to)1027-1036
Number of pages10
JournalIEEE Transactions on Signal Processing
Issue number3
Publication statusPublished - 1 Mar 2011

Keywords / Materials (for Non-textual outputs)

  • approximation
  • classical F-test
  • dynamical systems
  • information theoretic test
  • linear autoregressive models
  • novelty detection
  • perturbative expansion
  • real time series data
  • statistical test
  • approximation theory
  • autoregressive processes
  • information theory
  • signal detection
  • statistical testing


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