Estimating a random walk using fuzzily recent data

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.