Fast Grouping of Time Series

摘要

In some examples, a time-series data set can be analyzed and grouped in a fast and efficient manner. For instance, fast grouping of multiple time-series into clusters can be implemented through data reduction, determining cluster population, and fast matching by locality sensitive hashing. In some situations, a user can select a level of granularity for grouping time-series into clusters, which can involve trade-offs between the number of clusters and the maximum distance between two time-series in a cluster.

主权项

1. A method comprising:
collecting a plurality of time-series, wherein each of the plurality of time-series comprises a series of numerical values; generating a plurality of feature vectors, wherein each of the plurality of feature vectors corresponds to one of the plurality of time-series; mapping between granularity levels and distance thresholds for clustering, based, at least in part, on a subset of the plurality of feature vectors; generating a plurality of seeds that corresponds to one of the granularity levels, based, at least in part, on the mapping; and assigning each of the plurality of time-series to one of the plurality of seeds.