DLDE combines dynamic local density estimation via Time Split Tree and ensemble learning to detect anomaly subsequences in time series with claimed accuracy gains over prior methods.
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Pith papers citing it
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cs.LG 2years
2019 2verdicts
UNVERDICTED 2representative citing papers
Two trend-capturing extensions to PAA are introduced using split-segment averages and binary strings, with a claimed lower-bound proof and reported gains on classification and anomaly detection tasks.
citing papers explorer
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Anomaly Subsequence Detection with Dynamic Local Density for Time Series
DLDE combines dynamic local density estimation via Time Split Tree and ensemble learning to detect anomaly subsequences in time series with claimed accuracy gains over prior methods.
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An Improvement of PAA on Trend-Based Approximation for Time Series
Two trend-capturing extensions to PAA are introduced using split-segment averages and binary strings, with a claimed lower-bound proof and reported gains on classification and anomaly detection tasks.