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arXiv · 2512.05721

BERTO: Intent-Driven Network Time Series Forecasting via Natural Language Operator Preferences

Nitin Priyadarshini Shankar·Vaibhav Singh·Sheetal Kalyani·Christian Maciocco
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Abstract

Traditional cellular traffic forecasting models are optimized for minimizing symmetric errors, leaving them indifferent to shifting operational priorities. To bridge this gap, we introduce BERTO, a BERT-based framework for traffic prediction and energy optimization in cellular networks. Built on transformer architectures, BERTO achieves high prediction accuracy while enabling a single fine-tuned model to operate across multiple forecasting regimes via natural-language operator prompts. By combining a Balancing Loss Function (BLF) with prompt-based conditioning, BERTO adaptively shifts its forecasting bias toward underprediction or overprediction depending on the operator's desired trade-off between power savings and service quality. This allows the same model to dynamically generate different decision-aware forecasts without retraining or modifying model parameters. Experiments on real-world datasets demonstrate that BERTO can operate across a flexible range of approximately 1.4 kW in power consumption while balancing 9x variation in service level agreement (SLA) violations, making it well suited for intelligent RAN deployments.

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Nitin Priyadarshini Shankar, Vaibhav Singh, Sheetal Kalyani, Christian Maciocco. 2026-05-19. BERTO: Intent-Driven Network Time Series Forecasting via Natural Language Operator Preferences. https://arxiv.org/abs/2512.05721

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