Abstract
Periodical time series data can reveal temporal patterns as well as anomalies. Although these patterns appear in different ranges with different measurements, the variables in a dataset can be associated with and affect each other. When the size of datasets becomes large, monitoring and analyzing these data become challenging. We propose a visual analytics framework that is capable of transforming a large number of multivariate time serial data into manageable visual representations that enable analysts to identify and monitor anomalies and trends. After sorting the data in the temporal and categorical directions, we extract the temporal possibility range patterns and anomalies from each variable. Associated variables typically include similar anomalies. Therefore, we employ a hierarchical clustering algorithm to group the variables with similar anomaly appearances. Through our interactive visualization toolset TreeRoses, we find that possibility ranges, anomaly, and its summary, and hierarchical association is perceptible to different degrees. We apply our method to a real-world periodical time series dataset to showcase how our framework effectively monitors anomalies in meteorological data.
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Tang, H., Wei, S., Zhou, Z. et al. TreeRoses: outlier-centric monitoring and analysis of periodic time series data. J Vis 22, 1005–1019 (2019). https://doi.org/10.1007/s12650-019-00586-1
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DOI: https://doi.org/10.1007/s12650-019-00586-1