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Non‐parametric smoothing and prediction for nonlinear circular time series
Author(s) -
Di Marzio Macro,
Panzera Agnese,
Taylor Charles C.
Publication year - 2012
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/j.1467-9892.2012.00794.x
Subject(s) - mathematics , series (stratigraphy) , smoothing , parametric statistics , time series , nonlinear system , parametric model , constant (computer programming) , asymptotic analysis , function (biology) , field (mathematics) , statistics , computer science , paleontology , physics , quantum mechanics , evolutionary biology , pure mathematics , biology , programming language
Not much research has been done in the field of circular time‐series analysis. We propose a non‐parametric theory for smoothing and prediction in the time domain for circular time‐series data. Our model is based on local constant and local linear fitting estimates of a minimizer of an angular risk function. Both asymptotic arguments and empirical examples are used to describe the accuracy of our methods.