Abstract Broadband service providers readily use time-series models to forecast broadband related to minimise disruptions to customers but also from an operational perspective. Within a competitive market, minimizing future broadband faults is important for customer retention. Whilst broadband faults happen at the household level, broadband service providers typically forecast broadband faults at the regional scale, which hides local geographic heterogeneity. In this paper, we address the issue by applying Bayesian spatio-temporal statistical models to analyse the local geography of broadband faults for North West England. It drew on a unique aggregated broadband fault dataset, compiled by the large British commercial broadband service provider, Virgin Media. Our results support the proposed spatio-temporal model, which provided a significantly higher forecast accuracy and model fit, when compared to the standard time-series model. Incorporating geographic effects allowed the forecaster to identify how the spatial distribution of faults changes over time at a much finer spatial scale. We also found several significant predictors of broadband faults in the study area.

中文翻译：

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用贝叶斯时空统计模型探索宽带断层的地理

摘要 宽带服务提供商很容易使用时间序列模型来预测宽带，以最大限度地减少对客户的干扰，同时也从运营的角度来看。在竞争激烈的市场中，最大限度地减少未来的宽带故障对于保留客户非常重要。虽然宽带故障发生在家庭层面，但宽带服务提供商通常会在区域范围内预测宽带故障，这隐藏了当地的地理异质性。在本文中，我们通过应用贝叶斯时空统计模型来分析英格兰西北部宽带断层的局部地理来解决这个问题。它利用了一个独特的聚合宽带故障数据集，由英国大型商业宽带服务提供商 Virgin Media 编制。我们的结果支持提出的时空模型，与标准时间序列模型相比，它提供了显着更高的预测准确性和模型拟合。结合地理效应使预报员能够在更精细的空间尺度上确定断层的空间分布如何随时间变化。我们还在研究区发现了几个重要的宽带故障预测因子。