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Time Series Anomaly Detection Using Convolutional Neural Networks and Transfer Learning

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arxiv 1905.13628 v1 pith:G24YWQ35 submitted 2019-05-31 cs.LG cs.CVstat.ML

Tailai Wen , Roy Keyes This is my paper · ORCID
classification cs.LGcs.CVstat.ML
keywords seriestimeanomalydetectiondatalearningnetworksneural
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 4 Pith papers

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