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Freestyle Data Fitting and Global Temperatures

Published

Author(s)

Barend J. Thijsse, Bert W. Rust

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

Keywords

nonparametric fitting, signal and noise, smoothing splines

Citation

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 December 3, 2023)
Created January 30, 2008, Updated October 12, 2021