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• W4380853046 startingPage "e0286763" @default.
• W4380853046 abstract "The matrix profile (MP) is a data structure computed from a time series which encodes the data required to locate motifs and discords, corresponding to recurring patterns and outliers respectively. When the time series contains noisy data then the conventional approach is to pre-filter it in order to remove noise but this cannot apply in unsupervised settings where patterns and outliers are not annotated. The resilience of the algorithm used to generate the MP when faced with noisy data remains unknown. We measure the similarities between the MP from original time series data with MPs generated from the same data with noisy data added under a range of parameter settings including adding duplicates and adding irrelevant data. We use three real world data sets drawn from diverse domains for these experiments Based on dissimilarities between the MPs, our results suggest that MP generation is resilient to a small amount of noise being introduced into the data but as the amount of noise increases this reslience disappears." @default.
• W4380853046 created "2023-06-16" @default.
• W4380853046 creator A5068280279 @default.
• W4380853046 creator A5092179465 @default.
• W4380853046 date "2023-06-15" @default.
• W4380853046 modified "2023-09-25" @default.
• W4380853046 title "Calculating the matrix profile from noisy data" @default.
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• W4380853046 doi "https://doi.org/10.1371/journal.pone.0286763" @default.
• W4380853046 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37319138" @default.
• W4380853046 hasPublicationYear "2023" @default.
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