  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.
