什么是类似于 AWS 的 Kinesis Random Cut Forest 算法的用于时间序列流数据的 Google Clouds 异常检测解决方案？

Question

Im trying to implement an anomaly detection machine learning solution on GCP but finding it hard to find a specific solution using Google Cloud ML as with AWS' Random Cut Forest solution in Kinesis. Im streaming IoT temperature sensor data for water heaters.

Anyone know a tensorflow/google solution for this as my company only uses google stack?

Ive tried using sklearn models but none of them are implementable on producton for streaming data so have to use tensorflow but am novice. Any suggestions on a good flow to get this done?

Answer 1

I would suggest using Esper complex event processing engine if primary concern is the analysis of data stream and catching patterns in real time. It provides SQL like event processing language which runs as continuous query on floating data. Esper offers abstractions for correlation, aggregation and pattern detection. It is open source project and license is required if you want to run engine on multiple servers to achieve high availability.