摘 要
随着互联网技术的迅猛发展，网络流量呈现出爆炸式增长态势，准确预测网络流量对于网络资源优化配置、故障预警及服务质量保障具有重要意义。本研究旨在构建基于机器学习的网络流量预测模型，以提高预测精度并降低计算复杂度。首先收集了某地区实际网络流量数据作为样本集，涵盖多种典型应用场景，确保数据代表性。采用深度神经网络框架，引入长短期记忆网络（LSTM）处理时间序列特征，并结合卷积神经网络（CNN）提取局部模式，创新性地提出一种混合架构。针对传统方法中训练效率低下的问题，设计了自适应学习率调整机制与批量归一化技术，有效提升了模型收敛速度。实验结果表明，在不同测试场景下，该模型平均绝对误差较现有主流算法降低了约20%，且具备良好的泛化能力。此外，通过敏感性分析发现模型对特定参数变化具有较高鲁棒性，为实际应用提供了可靠保障。本研究不仅丰富了网络流量预测理论体系，更为智能网络管理提供了新的思路与技术支持，具有重要的学术价值和广阔的应用前景。
关键词：网络流量预测；机器学习；长短期记忆网络（LSTM）；卷积神经网络（CNN）；自适应学习率调整机制
Abstract
With the rapid development of Internet technology, network traffic has exhibited an explosive growth trend. Accurate prediction of network traffic is crucial for optimizing network resource allocation, predicting faults, and ensuring service quality. This study aims to construct a machine learning-based network traffic prediction model to improve prediction accuracy while reducing computational complexity. Actual network traffic data from a specific region were collected as the sample set, covering various typical application scenarios to ensure data representativeness. A deep neural network fr amework was employed, incorporating Long Short-Term Memory (LSTM) networks for handling time-series features and Convolutional Neural Networks (CNN) for extracting local patterns, thereby proposing an innovative hybrid architecture. To address the issue of low training efficiency in traditional methods, an adaptive learning rate adjustment mechanism and batch normalization techniques were designed, effectively enhancing the model's convergence speed. Experimental results demonstrate that under different testing scenarios, the proposed model reduces the mean absolute error by approximately 20% compared to existing mainstream algorithms, exhibiting excellent generalization capability. Additionally, sensitivity analysis reveals that the model possesses high robustness to specific parameter changes, providing a reliable guarantee for practical applications. This research not only enriches the theoretical system of network traffic prediction but also offers new insights and technical support for intelligent network management, holding significant academic value and broad application prospects.
Keywords：Network Traffic Prediction；Machine Learning；Long Short-Term Memory Network (Lstm)；Convolutional Neural Network (Cnn)；Adaptive Learning Rate Adjustment Mechanism
目 录
摘 要 I
Abstract II
引 言 1
第一章 网络流量预测模型概述 2
1.1 网络流量预测的重要性 2
1.2 机器学习在流量预测中的应用 2
1.3 现有研究的局限性分析 3
第二章 数据预处理与特征选择 5
2.1 流量数据的获取与清洗 5
2.2 特征提取与选择方法 5
2.3 数据集构建与划分策略 6
第三章 机器学习算法的选择与优化 8
3.1 常用机器学习算法比较 8
3.2 模型参数调优方法 8
3.3 算法性能评估指标 9
第四章 预测模型的构建与验证 11
4.1 模型架构设计原则 11
4.2 训练与测试过程 11
4.3 实验结果与分析讨论 12
结 论 14
参考文献 16
致 谢 17
