Baseline Algorithm Based on Gaussian Process Machine Learning
The baseline calculation is an important issue in the field of network monitoring.As to deal with the data,most researches just ignore the probability characteristics of the data,which fails to combine data distribution to predict the data and make the related processing and loses room for improvement in this field.Through the use of the compound kernel functions,the baseline is calculated in the proactive monitoring network.First,do clustering process for cycle data.Then we split the kernel functions for the global kernel functions and the local kernel functions,with using the cluster point to train the global kernel functions and the local points to the local kernel functions.Therefore,this article analyses the historical data's noise first,then make the prediction with the Gaussian process machine learning.The experiment,compared with other algorithms,shows improvement in the field of efficiency and accuracy.