In the telecom domain, precise forecasting of time series patterns, such as cell key performance indicators (KPIs), plays a pivotal role in enhancing service quality and operational efficiency. State-of-the-art forecasting approaches prioritize forecasting accuracy at the expense of computational performance, rendering them less suitable for data-intensive applications encompassing systems with a multitude of time series variables. To address this issue, we introduce QBSD, a live forecasting approach tailored to optimize the trade-off between accuracy and computational complexity. We have evaluated the performance of QBSD against state-of-the-art forecasting approaches on publicly available datasets. We have also extended this investigation to our curated network KPI dataset, now publicly accessible, to showcase the effect of dynamic operating ranges that varies with time. The results demonstrate that the proposed method excels in runtime efficiency compared to the leading algorithms available while maintaining competitive forecast accuracy.

翻译：在电信领域，对时间序列模式（如小区关键绩效指标KPIs）进行精准预测，对于提升服务质量和运营效率具有关键作用。当前最先进的预测方法以牺牲计算性能为代价追求预测精度，这使得它们难以适用于包含海量时间序列变量的数据密集型系统。为解决这一问题，我们提出了QBSD——一种旨在优化预测精度与计算复杂度之间权衡的实时预测方法。我们在公开数据集上评估了QBSD与当前最先进预测方法的性能，并将研究扩展至我们整理并现已公开的网络KPI数据集，以展示随时间变化的动态运行范围所产生的影响。结果表明，与现有领先算法相比，所提方法在保持竞争力的预测精度的同时，在运行效率方面表现卓越。