Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning

Tailai Wen; Roy Keyes

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arXiv · 1905.13628

Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning

Tailai Wen·Roy Keyes

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Abstract

Time series anomaly detection plays a critical role in automated monitoring systems. Most previous deep learning efforts related to time series anomaly detection were based on recurrent neural networks (RNN). In this paper, we propose a time series segmentation approach based on convolutional neural networks (CNN) for anomaly detection. Moreover, we propose a transfer learning framework that pretrains a model on a large-scale synthetic univariate time series data set and then fine-tunes its weights on small-scale, univariate or multivariate data sets with previously unseen classes of anomalies. For the multivariate case, we introduce a novel network architecture. The approach was tested on multiple synthetic and real data sets successfully.

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Tailai Wen, Roy Keyes. 2019-05-31. Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning. https://arxiv.org/abs/1905.13628

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