Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform-Reference-Cited by-同舟云学术

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Detecting anomalies in time series data via a deep learning algorithm combining wavelets, neural networks and Hilbert transform

Published:2017-11 Issue: Volume:85 Page:292-304
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Kanarachos Stratis,Christopoulos Stavros-Richard G.,Chroneos Alexander,Fitzpatrick Michael E.

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference44 articles.

1. The illustrated wavelet transform handbook;Addison,2002

2. Application of artificial bee colony algorithm in {TEC} seismo-ionospheric anomalies detection;Akhoondzadeh;Advances in Space Research,2015

3. Probabilistic anomaly detection in natural gas time series data;Akouemo;International Journal of Forecasting,2016

4. Wavelet neural networks: A practical guide;Alexandridis;Neural Networks,2013

5. Multi-scale anomaly detection algorithm based on infrequent pattern of time series;Chen;Journal of Computational and Applied Mathematics,2008

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