NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection

• URL: http://arxiv.org/abs/2101.02908v1
• Date: Fri, 8 Jan 2021 08:35:15 GMT
• Title: NVAE-GAN Based Approach for Unsupervised Time Series Anomaly Detection
• Authors: Liang Xu, Liying Zheng, Weijun Li, Zhenbo Chen, Weishun Song, Yue Deng, Yongzhe Chang, Jing Xiao, Bo Yuan
• Abstract summary: Time series anomaly detection is a common but challenging task in many industries. It is difficult to detect anomalies in time series with high accuracy, due to noisy data collected from real world. We propose our anomaly detection model: Time series to Image VAE (T2IVAE)
• Score: 19.726089445453734
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent studies, Lots of work has been done to solve time series anomaly detection by applying Variational Auto-Encoders (VAEs). Time series anomaly detection is a very common but challenging task in many industries, which plays an important role in network monitoring, facility maintenance, information security, and so on. However, it is very difficult to detect anomalies in time series with high accuracy, due to noisy data collected from real world, and complicated abnormal patterns. From recent studies, we are inspired by Nouveau VAE (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 the reconstruction error to detect anomalies. Besides, we also apply the Generative Adversarial Networks based techniques to T2IVAE training strategy, aiming to reduce the overfitting. We evaluate our model performance on three datasets, and compare it with other several popular models using F1 score. T2IVAE achieves 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 other comparison models.

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