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
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله

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
Journal: Knowledge-Based Systems - Volume 21, Issue 8, December 2008, Pages 807–812
نویسندگان
Xiaoqing Weng, Junyi Shen,