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

<ul>
<li><a href="http://dm.snu.ac.kr/static/docs/TR/SNUDM-TR-2015-03.pdf" rel="nofollow noreferrer">Variational Inference for On-line Anomaly Detection in
High-Dimensional Time Series</a></li>
<li><a href="https://arxiv.org/pdf/1612.06676.pdf" rel="nofollow noreferrer">Multivariate Industrial Time Series
with Cyber-Attack Simulation: Fault Detection Using an LSTM-based
Predictive Data Model</a></li>
</ul>
State-of-art for sensor's anomaly detection
<p>I am working on anomaly detection problem and I need your help and expertise. 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. </p>

<p>What would be the state-of-the-art approaches to that?</p>