State-of-art for sensor's anomaly detection

I am working on anomaly detection problem. I have a sensor that records episodic time series data. For example, once in a while, the sensor activates for 10 seconds and records values at millisecond interval. My task is to identify whether the recorded pattern is not normal. In other words, I need to detect anomalies in that pattern compared to other recorded patterns.

What would be the state-of-the-art approaches to that?

After doing my own research, the following methods proven to work very well in practice:

• Variational Inference for On-line Anomaly Detection in High-Dimensional Time Series
• Multivariate Industrial Time Series with Cyber-Attack Simulation: Fault Detection Using an LSTM-based Predictive Data Model