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
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;
Conference_Titel :
Fuzzy Information Processing Society, 2005. NAFIPS 2005. Annual Meeting of the North American
Print_ISBN :
0-7803-9187-X
DOI :
10.1109/NAFIPS.2005.1548522
