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In recent studies, Lots of work has been done to solve time series anomalydetection by applying Variational Auto-Encoders (VAEs). Time series anomalydetection is a very common but challenging task in many industries, which playsan important role in network monitoring, facility maintenance, informationsecurity, and so on. However, it is very difficult to detect anomalies in timeseries with high accuracy, due to noisy data collected from real world, andcomplicated abnormal patterns. From recent studies, we are inspired by NouveauVAE (NVAE) and propose our anomaly detection model: Time series to Image VAE(T2IVAE), an unsupervised model based on NVAE for univariate series,transforming 1D time series to 2D image as input, and adopting thereconstruction error to detect anomalies. Besides, we also apply the GenerativeAdversarial Networks based techniques to T2IVAE training strategy, aiming toreduce the overfitting. We evaluate our model performance on three datasets,and compare it with other several popular models using F1 score. T2IVAEachieves 0.639 on Numenta Anomaly Benchmark, 0.651 on public dataset from NASA,and 0.504 on our dataset collected from real-world scenario, outperforms othercomparison models.

NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection