ARI-LLM: Autoregressive Imputation for Network Traffic Matrix via Large Language Models

Abstract

The Traffic Matrix (TM) plays a vital role in a wide range of network management tasks, yet monitoring complete TMs is prohibitively expensive. Recent efforts focus on recovering incomplete TMs via learning-based models by learning from historical TM snapshots. However, existing approaches often suffer from poor generalization and require frequent retraining when the network environment evolves—let alone unseen networks. In this paper, we propose a novel TM imputation framework, named ARI-LLM, based on Large Language Models (LLMs). Inspired by human-like cognitive process of coarse-to-fine reasoning, our method incrementally refines the incomplete TM through a step-wise autoregressive mechanism, which is compatible with the pretraining paradigm of LLMs, endowing our model with powerful generalization and zero-shot capabilities. To ensure plug-and-play deployment across TM imputation tasks in dynamic environments, we craft a flow-aware tokenization scheme that supports scalable and topology-adaptive embeddings. Extensive experiments demonstrate that ARI-LLM significantly outperforms state-of-the-art methods on large-scale sparse TMs (over 90% missingness), reducing imputation error by up to 34.7%. It maintains strong performance whether given only limited TM knowledge (e.g., 20% observed flows, 5% training snapshots) or no prior knowledge at all on unseen networks.