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Author: Admin | 2025-04-28
AbstractTime series anomaly detection is one of the most active areas of research in data mining, with dozens of new approaches been suggested each year. In spite of all these creative solutions proposed for this problem, recent empirical evidence suggests that the time series discord, a relatively simple twenty-year old distance-based technique, remains among the state-of-art techniques. While there are many algorithms for computing the time series discords, they all have limitations. First, they are limited to the batch case, whereas the online case is more actionable. Second, these algorithms exhibit poor scalability beyond tens of thousands of datapoints. In this work we introduce DAMP, a novel algorithm that addresses both these issues. DAMP computes exact left-discords on fast arriving streams, at up to 300,000 Hz using a commodity desktop. This allows us to find time series discords in datasets with trillions of datapoints for the first time. We will demonstrate the utility of our algorithm with the most ambitious set of time series anomaly detection experiments ever conducted. We will further show that our speedup improvements can be applied in the multidimensional case. Access this article Log in via an institution Subscribe and save Get 10 units per month Download Article/Chapter or eBook 1 Unit = 1 Article or 1 Chapter Cancel anytime Subscribe now Buy Now Price excludes VAT (USA) Tax calculation will be finalised during checkout. Instant access to the full article PDF. Similar content being viewed by others Explore related subjects Discover the latest articles, news and stories from top researchers in related subjects. NotesNote that some papers misattribute the success of their anomaly detection to the Matrix Profile or to HOTSAX, but these are simple different algorithms to compute time series discords.This observation is true for heartbeats, gaits, machine cycles etc. One exception is for
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