融合卷积、LSTM与Transformer，提升网络流量预测精度。
ConvLSTMTransNet: A Hybrid Deep Learning Approach for Internet Traffic Telemetry
- 结合CNN、LSTM与Transformer捕捉时空特征
- 相比RNN/LSTM/GRU提升约10%预测准确率
- 适合需要高精度流量预测的网络运维场景
本文提出一种新型混合深度学习模型ConvLSTMTransNet，用于时间序列预测，特别针对互联网流量遥测数据。该模型融合卷积神经网络（CNN）、长短期记忆网络（LSTM）和Transformer编码器，以捕捉时间序列中复杂的时空关系。在真实高速路由器端口流量数据上，与RNN、LSTM和门控循环单元（GRU）三类基线模型对比，采用平均绝对误差（MAE）、均方根误差（RMSE）和加权绝对百分比误差（WAPE）作为评估指标。结果表明，ConvLSTMTransNet在预测精度上相较基线模型显著提升约10%。其优异表现源于创新的架构设计，增强了对时序依赖性的建模能力及对流量数据空间特征的提取能力。研究证实，针对互联网流量复杂性设计先进架构，是实现更精准预测的关键。
原文摘要 · Abstract (English)
In this paper, we present a novel hybrid deep learning model, named ConvLSTMTransNet, designed for time series prediction, with a specific application to internet traffic telemetry. This model integrates the strengths of Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer encoders to capture complex spatial-temporal relationships inherent in time series data. The ConvLSTMTransNet model was evaluated against three baseline models: RNN, LSTM, and Gated Recurrent Unit (GRU), using real internet traffic data sampled from high-speed ports on a provider edge router. Performance metrics such as Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Weighted Absolute Percentage Error (WAPE) were used to assess each model's accuracy. Our findings demonstrate that ConvLSTMTransNet significantly outperforms the baseline models by approximately 10% in terms of prediction accuracy. ConvLSTMTransNet surpasses traditional models due to its innovative architectural features, which enhance its ability to capture temporal dependencies and extract spatial features from internet traffic data. Overall, these findings underscore the importance of employing advanced architectures tailored to the complexities of internet traffic data for achieving more precise predictions.
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