论文标题
检测gamlss-akaike-weights得分的异常时间序列
Detecting Anomalous Time Series by GAMLSS-Akaike-Weights-Scoring
论文作者
论文摘要
一个可扩展的统计框架，用于检测异常时间序列，其中包括具有重尾分布和非平稳性的统计时间序列，该框架是基于受惩罚的似然分布回归引入的。具体而言，用于位置，比例和形状的广义添加剂模型用于推断由参数分布定义的样本路径表示，该参数分布具有由基本函数组成的参数。然后将Akaike权重应用于每个模型和时间序列，产生一种概率度量，可有效地用于对异常时间序列进行分类和排名。还给出了数学上的阐释，以证明在合适的模型嵌入中提出的Akaike重量评分是合理的，以渐近地识别异常时间序列。评估多个模拟和现实数据集上方法的研究还可以证实，可以获得高精度，以检测许多不同且复杂的形状异常。这两个代码都实现了用于在本地计算机上运行的GAW，并且本文中引用的数据集可在线获得。
An extensible statistical framework for detecting anomalous time series including those with heavy-tailed distributions and non-stationarity in higher-order moments is introduced based on penalized likelihood distributional regression. Specifically, generalized additive models for location, scale, and shape are used to infer sample path representations defined by a parametric distribution with parameters comprised of basis functions. Akaike weights are then applied to each model and time series, yielding a probability measure that can be effectively used to classify and rank anomalous time series. A mathematical exposition is also given to justify the proposed Akaike weight scoring under a suitable model embedding as a way to asymptotically identify anomalous time series. Studies evaluating the methodology on both multiple simulations and real-world datasets also confirm that high accuracy can be obtained detecting many different and complex types of shape anomalies. Both code implementing GAWS for running on a local machine and the datasets referenced in this paper are available online.
