Multivariate Time-series Anomaly Detection using SeqVAE-CNN Hybrid Model

Anomaly detection has been recognized as an important research area in many industries such as Information Technology, manufacturing, finance, etc. Recently, diverse research for anomaly detection has been conducted utilizing current deep learning methods including machine learning algorithms. However, multivariate time-series anomaly detection can be challenging problems because of the imbalance of anomaly data and the complexity of multivariate. In this paper, we propose a SeqVAE-CNN model based on deep learning using an unsupervised approach. Our model combines Variational Autoencoder (VAE) with Convolutional Neural Networks (CNN) as utilizing Seq2Seq structure to capture temporal correlations and spatial features in multivariate time-series. To demonstrate the performance of our approaches, we evaluate our model on 8 datasets from various domains. The experimental results demonstrate that our model has better performance of anomaly detection than other models by recording the highest AUROC and F1 scores on six of the eight datasets.