A Perturbative Approach to Novelty Detection in Autoregressive Models
A Perturbative Approach to Novelty Detection in Autoregressive Models
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DOI：
10.1109/tsp.2010.2094609
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发表时间：
2011-03
影响因子：
5.4
通讯作者：
M. Filippone;G. Sanguinetti
中科院分区：
文献类型：
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作者：
M. Filippone;G. Sanguinetti
我们提出了一种新的方法来进行新奇的检测线性自回归模型的动力系统。该方法是基于一个扰动扩展到一个统计测试，其领先的长期是经典的F-检验，其O（1/n）的校正可以近似为一个函数的训练点的数量和模型的顺序。该方法可以被证明是一个近似的信息理论测试。我们证明了几个合成的例子，第一次校正的F-检验可以显着提高系统的假阳性率的控制。我们还在一些真实的时间序列数据上测试了该方法，证明该方法在检测新奇事物方面仍然保持了良好的准确性。
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.