VAE-TCN hybrid model for KPI Anomaly Detection

The unsupervised anomaly detection in KPI (Key Performance Indicator) series has been an active research area due to its enormous potential for application in industry. KPI series representation, reconstruction, and forecasting have made extraordinary progress in existing work. However, long-term temporal patterns prohibit the model from learning reliable dependencies. To this end, we propose a novel approach based on VAE-TCN hybrid model. Our model uses VAE (variational automatic coder) to learn robust local features in a short window, and uses TCN (temporal convolution network) to estimate the long-term correlation in the sequence based on the features inferred by VAE module. Extensive experiments on various public benchmarks demonstrate that our method has achieved the state-of-the-art performance.