Chebyshev similarity match between uncertain time series
type: Item
instance of: scholarly article
title: Chebyshev similarity match between uncertain time series
zbMATH Open document ID: 1395.62283
DOI: 10.1155/2015/105128
author: Wei Wang
author: Dingjia Liu
author: Guohua Liu
published in: Mathematical Problems in Engineering
publication date: 2018-08-27T00:00:00Z
review text: Summary: In real application scenarios, the inherent impreciseness of sensor readings, the intentional perturbation of privacy-preserving transformations, and error-prone mining algorithms cause much uncertainty of time series data. The uncertainty brings serious challenges for the similarity measurement of time series. In this paper, we first propose a model of uncertain time series inspired by Chebyshev inequality. It estimates possible sample value range and central tendency range in terms of sample estimation interval and central tendency estimation interval, respectively, at each time slot. In comparison with traditional models adopting repeated measurements and random variable, Chebyshev model reduces overall computational cost and requires no prior knowledge. We convert Chebyshev uncertain time series into certain time series matrix; therefore noise reduction and dimensionality reduction are available for uncertain time series. Secondly, we propose a new similarity matching method based on Chebyshev model. It depends on overlaps between two sample estimation intervals and overlaps between central tendency estimation intervals from different uncertain time series. At the end of this paper, we conduct an extensive experiment and analyze the results by comparing with prior works.
Mathematics Subject Classification ID: 62M10
zbMATH DE Number: 6925486
Wikidata QID: Q59118418
describes a project that uses: itsmr
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2015/105128
OpenAlex ID: W1956151890
cites work: Managing and mining uncertain data
cites work: Q5520460
cites work: Q4718452
cites work: Q3996161
cites work: Ten Lectures on Wavelets
description: scientific article; zbMATH DE number 6925486