  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.
