Title: Improving Internet Traffic Matrix Prediction via Time Series Clustering Show Chinese title

Title: 通过时间序列聚类改进互联网流量矩阵预测

Abstract: We present a novel framework that leverages time series clustering to improve internet traffic matrix (TM) prediction using deep learning (DL) models. Traffic flows within a TM often exhibit diverse temporal behaviors, which can hinder prediction accuracy when training a single model across all flows. To address this, we propose two clustering strategies, source clustering and histogram clustering, that group flows with similar temporal patterns prior to model training. Clustering creates more homogeneous data subsets, enabling models to capture underlying patterns more effectively and generalize better than global prediction approaches that fit a single model to the entire TM. Compared to existing TM prediction methods, our method reduces RMSE by up to 92\% for Abilene and 75\% for G\'EANT. In routing scenarios, our clustered predictions also reduce maximum link utilization (MLU) bias by 18\% and 21\%, respectively, demonstrating the practical benefits of clustering when TMs are used for network optimization. Show Chinese abstract

Abstract: 我们提出了一种新的框架，利用时间序列聚类来改进使用深度学习（DL）模型的互联网流量矩阵（TM）预测。 TM中的流量通常表现出多样的时间行为，这在跨所有流量训练单一模型时可能会影响预测准确性。 为了解决这个问题，我们提出了两种聚类策略，源聚类和直方图聚类，在模型训练之前将具有相似时间模式的流量分组。 聚类创建了更同质的数据子集，使模型能够更有效地捕捉潜在模式，并比全局预测方法表现得更好，这些方法将单一模型拟合到整个TM。 与现有的TM预测方法相比，我们的方法将Abilene的RMSE降低了高达92%，将GÉANT的RMSE降低了高达75%。 在路由场景中，我们的聚类预测分别将最大链路利用率（MLU）偏差减少了18%和21%，证明了在TM用于网络优化时聚类的实际优势。