Automatic detection of anomalies in space- and time-varying measurements is an important tool in several fields, e.g., fraud detection, climate analysis, or healthcare monitoring. We present an algorithm for detecting anomalous regions in multivariate spatio-temporal time-series, which allows for spotting the interesting parts in large amounts of data, including video and text data. In opposition to existing techniques for detecting isolated anomalous data points, we propose the “Maximally Divergent Intervals” (MDI) framework for unsupervised detection of coherent spatial regions and time intervals characterized by a high Kullback-Leibler divergence compared with all other data given. In this regard, we define an unbiased Kullback-Leibler divergence that allows for ranking regions of different size and show how to enable the algorithm to run on large-scale data sets in reasonable time using an interval proposal technique. Experiments on both synthetic and real data from various domains, such as climate analysis, video surveillance, and text forensics, demonstrate that our method is widely applicable and a valuable tool for finding interesting events in different types of data.

中文翻译：

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检测时空异常检测的最大发散区域


自动检测时空变化测量中的异常是欺诈检测、气候分析或医疗保健监控等多个领域的重要工具。我们提出了一种用于检测多元时空时间序列中的异常区域的算法，该算法可以在大量数据（包括视频和文本数据）中发现有趣的部分。与检测孤立异常数据点的现有技术相反，我们提出了“最大发散间隔”（MDI）框架，用于无监督检测相干空间区域和时间间隔，其特征是与给定的所有其他数据相比具有高 Kullback-Leibler 发散度。在这方面，我们定义了一个无偏的 Kullback-Leibler 散度，它允许对不同大小的区域进行排名，并展示如何使用区间提议技术使算法能够在合理的时间内在大规模数据集上运行。对气候分析、视频监控和文本取证等各个领域的合成数据和真实数据进行的实验表明，我们的方法具有广泛的适用性，并且是在不同类型的数据中查找有趣事件的宝贵工具。