Anomaly detection and analysis by a gradient boosting trees and neural network ensemble model

In this paper, we describe a method for predicting product characteristics, its evaluation results, and application examples. Process equipment data is selected as an explanatory variable. By using an ensemble of gradient boosting trees and neural networks, we were able to construct a prediction model with higher accuracy than the conventional model. In addition, anomaly detection and analysis based on this prediction model are discussed.