摘 要
随着互联网的迅猛发展，网络流量呈现出爆发式增长态势，准确预测网络流量对于网络规划、资源分配及故障预警等具有重要意义。本研究旨在构建基于机器学习的网络流量预测模型，以提高预测精度和时效性。选取了多种典型机器学习算法，包括支持向量机、随机森林、长短期记忆神经网络等，并针对网络流量数据的特点进行了算法优化改进。采用某实际网络环境下的真实流量数据集进行实验验证，在数据预处理阶段运用滑动窗口方法提取特征序列，通过交叉验证确定最优模型参数。实验结果表明，所构建的模型在不同时间尺度下均能有效预测网络流量，相较于传统统计方法，平均绝对误差降低约30%，均方根误差减少近25%。该模型能够适应网络流量的动态变化特性，具备较强的泛化能力。创新点在于融合多种机器学习算法优势并结合网络流量自身特性进行针对性优化，为网络流量预测提供了新的思路与方法，对提升网络服务质量具有重要参考价值。
关键词：网络流量预测 机器学习算法 模型优化
Abstract
With the rapid development of the Internet, network traffic has exhibited an explosive growth trend. Accurate prediction of network traffic is of great significance for network planning, resource allocation, and fault warning. This study aims to construct a network traffic prediction model based on machine learning to improve prediction accuracy and timeliness. Multiple typical machine learning algorithms, including Support Vector Machines (SVM), Random Forests, and Long Short-Term Memory (LSTM) neural networks, were selected and optimized according to the characteristics of network traffic data. Real traffic datasets from an actual network environment were used for experimental validation. In the data preprocessing stage, the sliding window method was employed to extract feature sequences, and cross-validation was used to determine the optimal model parameters. The experimental results show that the constructed model can effectively predict network traffic at different time scales. Compared with traditional statistical methods, the Mean Absolute Error (MAE) was reduced by approximately 30%, and the Root Mean Square Error (RMSE) decreased by nearly 25%. The model demonstrates adaptability to the dynamic changes in network traffic and possesses strong generalization capabilities. The innovation lies in integrating the advantages of multiple machine learning algorithms and optimizing them specifically for the characteristics of network traffic, providing new approaches and methods for network traffic prediction, which holds important reference value for enhancing network service quality.
Keyword:Network Traffic Prediction Machine Learning Algorithm Model Optimization
目 录
引言 1
1网络流量预测的背景与意义 1
1.1网络流量预测的重要性 1
1.2传统预测方法的局限性 2
1.3机器学习的优势与应用 2
2机器学习算法的选择与优化 3
2.1常用机器学习算法综述 3
2.2算法选择的标准与依据 3
2.3模型优化与参数调整 4
3数据处理与特征工程 5
3.1数据收集与预处理 5
3.2特征选择与提取 5
3.3数据集构建与划分 6
4模型评估与性能分析 6
4.1评估指标体系构建 6
4.2实验设计与结果分析 7
4.3性能对比与改进方向 7
结论 8
参考文献 9
致谢 9
