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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2018

04.08.2017

Novelty Detection in Time Series Using Self-Organizing Neural Networks: A Comprehensive Evaluation

verfasst von: Leonardo Aguayo, Guilherme A. Barreto

Erschienen in: Neural Processing Letters | Ausgabe 2/2018

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Abstract

In this survey paper, we report the results of a comprehensive study involving the application of dynamic self-organizing neural networks (SONNs) to the problem of novelty detection in time series data. The study is comprised of three main parts. In the first part, we aim at evaluating how the performances of nonrecurrent dynamic SONNs are influenced by the introduction of different short-term memory kernels, such as Gamma, Gamma II and Laguerre, in the network input. In the second part, we analyze the performances of recurrent dynamic SONNs with the goal of inferring if they possess or not any competitive advantage over nonrecurrent architectures for novelty detection in time series. Finally, in the third part of the study, we introduce an alternative approach for dynamic SONN-based novelty detection by revisiting the operator map framework introduced by Lampinen and Oja (Proceedings of the 6th Scandinavian conference on image analysis (SCIA’89), pp 120–127, 1989) and Lehtimäki et al. (Proceedings of the joint international conference on artificial neural networks and neural information processing (ICANN/ICONIP’2003), pp 622–629, 2003). This framework allows the design of dynamic SONNs whose neurons are regarded as adaptive local linear models. In this case, novel/abnormal patterns are detected based on the statistics of prediction errors of the local models. A comprehensive performance comparison involving several nonrecurrent and recurrent dynamic SONN architectures is carried out using both synthetic and real-world time series data.
Vorheriger Artikel Nonlinear Dimensionality Reduction for Data with Disconnected Neighborhood Graph
Nächster Artikel Competitive Brain Emotional Learning

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Fußnoten
1
Also known as Shewhart charts or process-behavior charts.
 
2
Difference between the observed and the predicted value of the variable of interest.
 
3
By definition, the (unilateral) Z-transform of a discrete-time signal \(\{x(n)\}_{n=0}^{\infty }\) is defined as \(X(z) = \mathcal {Z}\{x(n)\} = \sum _{n=0}^{\infty } x(n)z^{-n}\), where n is an integer and \(z=Ae^{j\phi }=A(\cos \phi + j\sin \phi )\). In words, the Z-transform converts a time series, which is a sequence of real (or complex) numbers, into a complex frequency domain representation.
 
4
Roots of the polynomial in the denominator of \(G^0(z)\) and G(z). For Eq. (37), the pole is found by doing \(z-(1-\lambda )=0\).
 
5
For the remaining parts of this paper, the label NORMAL will be used for known or ordinary patterns; and ABNORMAL for the novel, anomalous or unknown ones.
 
6
The percentile of a distribution of values is a number \(N_{\alpha }\) such that a percentage \(100(1-\alpha )\) of the sample values are less than or equal to \(N_{\alpha }\).
 
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Metadaten
Titel
Novelty Detection in Time Series Using Self-Organizing Neural Networks: A Comprehensive Evaluation
verfasst von
Leonardo Aguayo
Guilherme A. Barreto
Publikationsdatum
04.08.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9679-2

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