The timely detection of anomalies is essential in the telecom domain as it facilitates the identification and characterization of irregular patterns, abnormal behaviors, and network anomalies, contributing to enhanced service quality and operational efficiency. Precisely forecasting and eliminating predictable time series patterns constitutes a vital component of time series anomaly detection. While the state-of-the-art methods aim to maximize forecasting accuracy, the computational performance takes a hit. In a system composed of a large number of time series variables, e.g., cell Key Performance Indicators (KPIs), the time and space complexity of the forecasting employed is of crucial importance. Quartile-Based Seasonality Decomposition (QBSD) is a live forecasting method proposed in this paper to make an optimal trade-off between computational complexity and forecasting accuracy. This paper compares the performance of QBSD to the state-of-the-art forecasting methods and their applicability to practical anomaly detection. To demonstrate the efficacy of the proposed solution, experimental evaluation was conducted using publicly available datasets as well as a telecom KPI dataset.

翻译：及时检测异常在电信领域至关重要，因为它有助于识别和表征不规则模式、异常行为及网络异常，从而提升服务质量和运营效率。精确预测并消除可预测的时间序列模式是时间序列异常检测的关键组成部分。虽然现有最优方法旨在最大化预测精度，但计算性能会受到影响。在包含大量时间序列变量（如小区关键性能指标KPI）的系统中，预测所采用的时间和空间复杂度至关重要。本文提出了一种基于四分位数的季节性分解（QBSD）实时预测方法，以在计算复杂度与预测精度之间实现最优权衡。本文比较了QBSD与现有最优预测方法的性能及其在实际异常检测中的适用性。为验证所提方法的有效性，我们使用公开数据集及电信KPI数据集进行了实验评估。