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BEATS: Blocks of Eigenvalues Algorithm for Time series Segmentation
2018
IEEE Transactions on Knowledge and Data Engineering
The massive collection of data via emerging technologies like the Internet of Things (IoT) requires finding optimal ways to reduce the observations in the time series analysis domain. The IoT time series require aggregation methods that can preserve and represent the key characteristics of the data. In this paper, we propose a segmentation algorithm that adapts to unannounced mutations of the data (i.e. data drifts). The algorithm splits the data streams into blocks and groups them in square
doi:10.1109/tkde.2018.2817229
fatcat:qt6qe5j5gnhkdg4jn3ie5bxyoy