回归模型中基于机器学习的流量预测算法

Based on Machine Learning Traffic Prediction Algorithm in Regression Model

该文在基于机器学习的流量预测算法中，详细研究基于回归模型的预测算法，将机器学习算法引入到网络流量预测的领域，提出以不同算子来描述网络流量的非线性特性，同时针对网络流量特性，我们提出用主成分分析方法来作为预处理和为特征保留权重分布；最后针对相关实验中发现的过匹配现象提出自适应权重更新规则。

In this paper, based on machine learning flow prediction algorithm, based on a detailed study of the regression models for predicting algorithm, machine learning algorithm is introduced into the network traffic forecasting, put forward different weak regression operator is used to describe the flow of network non-linear characteristics, in view of network traffic self-similarity, we present two different mechanisms, using principal component analysis as preprocessing and for each one-dimensional characteristics retained a group of weight distribution; at the same time, the experiment was found in the matched phenomenon presents an adaptive weight updating guidelines.