Discovering motifs in time series data has been widely explored. Various techniques have been developed to tackle this problem. However, when it comes to spatial-time series, a clear gap can be observed according to the literature review. This paper tackles such a gap by presenting an approach to discover and rank motifs in spatial-time series, denominated Combined Series Approach (CSA). CSA is based on partitioning the spatial-time series into blocks. Inside each block, subsequences of spatial-time series are combined in a way that hash-based motif discovery algorithm is applied. Motifs are validated according to both temporal and spatial constraints. Later, motifs are ranked according to their entropy, the number of occurrences, and the proximity of their occurrences. The approach was evaluated using both synthetic and seismic datasets. CSA outperforms traditional methods designed only for time series. CSA was also able to prioritize motifs that were meaningful both in the context of synthetic data and also according to seismic specialists.

中文翻译：

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时空图案发现

在时间序列数据中发现主题已被广泛探索。已经开发出各种技术来解决这个问题。但是，当涉及到时空序列时，根据文献综述可以观察到明显的差距。本文通过提出一种在空间-时间序列中发现和排序主题的方法（称为组合序列方法（CSA））来解决这种差距。CSA基于将时空序列划分为块的基础。在每个块内部，以应用基于散列的基元发现算法的方式组合时空序列的子序列。根据时间和空间约束对主题进行验证。随后，根据主题的熵，出现次数和出现的接近程度对主题进行排序。使用综合和地震数据集评估了该方法。CSA优于仅针对时间序列设计的传统方法。CSA还能够对主题进行优先排序，这些主题在合成数据的背景下以及地震专家看来都是有意义的。