Time series forecasting plays a critical role in many domains, yet existing methods often demand domain-specific expertise and large volumes of historical data. In this work, we introduce TELE-PLM, a novel framework that adapts pre-trained language models (PLMs) for time series forecasting, with a particular focus on telecommunication revenue prediction. Our approach transforms raw time series into text-based prototype representations and incorporates a Prompt-as-Prefix mechanism to align continuous temporal signals with the discrete token space of PLMs. We construct and release a clean, structured dataset derived from MobiFone’s transaction records, enabling reproducible research in telecom forecasting. Extensive experiments compare TELE-PLM using BERT, GPT-2, and T5 against strong baselines such as TimesNet and TimeMixer. Results demonstrate that TELE-PLM significantly outperforms traditional time series models, with T5 achieving the lowest forecasting error (MASE = 2.6846), highlighting the advantage of leveraging text-to-text architectures for temporal prediction. These findings underscore the potential of PLMs as generalizable and data-efficient forecasters. Future work will explore multi-step forecasting, cross-domain adaptation, and hybrid architectures that integrate PLMs with temporal inductive biases for enhanced performance.