Search for top-k discords in streaming time series efficiently.
Abstract
The identification of anomalies in streaming time series has lately gained a lot of interest. The goal is to determine the most discordant subsequence (discord) in a time-series stream that may come at rapid speed. The fact that finding the top-k discords is more valuable than finding the most unusual subsequence because users may choose from the top-k discords rather than just one. As a result, the research proposes an effective strategy for searching for top-k discords in streaming time series. To eliminate superfluous distance computations, the method employs a lower bound threshold, a lower bounding methodology on a popular dimensionality reduction transform, and a cutting-edge way of distance computation between two time-series subsequences.
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