DocumentCode
2641597
Title
Estimating a random walk using fuzzily recent data
Author
Whalen, Thomas ; Zhang, G. Peter
Author_Institution
Dept. of Managerial Sci., Georgia State Univ., Atlanta, GA, USA
fYear
2005
fDate
26-28 June 2005
Firstpage
139
Lastpage
144
Abstract
This paper presents a method for estimating a random walk which is observed subject to additive noise. The method is based on an optimal weighted average, conceptualized as defining a fuzzy set of "recent" data. A Monte Carlo experiment compares the method\´s effectiveness against the naive method, ordinary least squares regression, and regression with fuzzily recent data.
Keywords
estimation theory; fuzzy set theory; regression analysis; time series; Monte Carlo experiment; data estimation; fuzzily recent data; fuzzy set memberships; naive method; optimal weighted average; ordinary least squares regression; random walk; time series; Additive noise; Fuzzy sets; Least squares approximation; Least squares methods; Monte Carlo methods; Predictive models; State estimation; Yttrium; Estimation; Fuzzy Set Memberships; Time Series; regression;
fLanguage
English
Publisher
ieee
Conference_Titel
Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American
Print_ISBN
0-7803-9187-X
Type
conf
DOI
10.1109/NAFIPS.2005.1548522
Filename
1548522
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