Thijsse, B.
and Rust, B.
(2008),
Freestyle Data Fitting and Global Temperatures, Computing in Science & Engineering, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=50869
(Accessed April 25, 2024)
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Freestyle Data Fitting and Global Temperatures
Published
Author(s)
Barend J. Thijsse,
Abstract
This paper presents a method for separating signal (trend) from noise in a set of measured bivariate data when there is no mathematical model for that signal. The algorithm models the signal with a smoothing spline for which the number and location of the knots are chosen to optimize the separation into deterministic and random components. This is done by generating a collection of different trial splines and determining which gives residuals with statistical properties most consistent with random noise, in which even autocorrelation is detected. It describes a computer program spline2 which implements the algorithm and applies it to three real world example problems.Citation
Computing in Science & Engineering
Volume
10
Issue
1
Pub Type
Journals
Download Paper
Keywords
nonparametric fitting, signal and noise, smoothing splines
Citation
Created January 30, 2008, Updated October 12, 2021