We develop a Bayesian hierarchical model to identify communities of time series. Fitting the model provides an end-to-end community detection algorithm that does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approach naturally supports multiscale community detection and the selection of an optimal scale using model comparison. We study the properties of the algorithm using synthetic data and apply it to daily returns of constituents of the S&P100 index and climate data from U.S. cities.

中文翻译：

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在不观察边缘的情况下在网络中进行社区检测。

我们开发了一种贝叶斯分层模型来识别时间序列社区。拟合模型提供了一种端到端的社区检测算法，该算法不提取信息作为点估计序列，而是将不确定性从原始数据传播到社区标签。我们的方法自然支持多尺度社区检测和使用模型比较选择最佳尺度。我们使用合成数据研究该算法的性质，并将其应用于S＆P100指数成分的每日收益以及来自美国城市的气候数据。