کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
404061 677385 2008 6 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Detecting outlier samples in multivariate time series dataset
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Detecting outlier samples in multivariate time series dataset
چکیده انگلیسی

Multivariate time series (MTS) samples which differ significantly from other MTS samples are referred to as outlier samples. In this paper, an algorithm designed to efficiently detect the top n outlier samples in MTS dataset, based on Solving Set, is proposed. An extended Frobenius Norm is used to compute the distance between MTS samples. The outlier score of MTS sample is the sum of the distances from its k nearest neighbors. The time complexity of the algorithm is subquadratic. We conduct experiments on two real-world datasets, stock market dataset and BCI (Brain Computer Interface) dataset. The experiment results show the efficiency and effectiveness of the algorithm.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Knowledge-Based Systems - Volume 21, Issue 8, December 2008, Pages 807–812
نویسندگان
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