arXiv:2603.11475v2 Announce Type: replace Abstract: Accurate prediction of multivariate time series is essential for emerging network intelligent control, observability, and management functions. Existing statistical-based and shallow machine learning models have shown limited prediction capabilities on multivariate time series. They prioritize improvements in average prediction accuracy, while overlooking heterogeneous dependency structures and performance variability across individual time series. Recent advances in large language models have introduced new directions for multivariate time s
The increasing complexity and scale of network infrastructure, coupled with advancements in deep learning and large language models, necessitate more sophisticated predictive capabilities.
Improved traffic prediction through advanced AI will lead to more efficient, resilient, and autonomous network management, impacting critical infrastructure and economic operations.
Traditional statistical and shallow machine learning models for multivariate time series prediction are being surpassed by deep learning and LLM-inspired network-temporal models, offering greater accuracy and heterogeneity capture.
- · Telecommunications companies
- · Cloud service providers
- · AI/ML research institutions
- · Network infrastructure developers
- · Developers of traditional statistical prediction models
- · Companies reliant on less efficient network management
Networks become more optimized, reducing latency and increasing throughput.
Enhanced network intelligence enables more robust and automated cybersecurity responses and resource allocation.
The application of such models could extend beyond traffic to other complex multivariate systems, such as energy grids or supply chains, creating new efficiencies across sectors.
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Read at arXiv cs.LG