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

作者：

Highlights：

• Design of a transferable time series anomaly detection method.

• Novel deep neural network structure facilitates learning short and long-term pattern interdependencies.

• Detection of anomalies in the Seismic Electrical Signal for predicting earthquake activity.

• Detection of road anomalies using smartphone data, facilitating crowdsourcing applications.

摘要

•Design of a transferable time series anomaly detection method.•Novel deep neural network structure facilitates learning short and long-term pattern interdependencies.•Detection of anomalies in the Seismic Electrical Signal for predicting earthquake activity.•Detection of road anomalies using smartphone data, facilitating crowdsourcing applications.

论文关键词：Anomaly detection,Deep learning,Receiver operating characteristics

论文评审过程：Received 9 February 2016, Revised 6 April 2017, Accepted 14 April 2017, Available online 26 April 2017, Version of Record 23 May 2017.

论文官网地址：https://doi.org/10.1016/j.eswa.2017.04.028