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Time‐varying multi‐regime models fitting by genetic algorithms
Author(s) -
Battaglia Francesco,
Protopapas Mattheos K.
Publication year - 2011
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.1467-9892.2010.00695.x
Subject(s) - series (stratigraphy) , algorithm , identification (biology) , mathematics , nonlinear system , piecewise linear function , genetic algorithm , piecewise , linear model , system identification , mathematical optimization , computer science , data modeling , statistics , paleontology , mathematical analysis , botany , physics , geometry , quantum mechanics , biology , database
Many time series exhibit both nonlinearity and non‐stationarity. Though both features have been often taken into account separately, few attempts have been proposed for modelling them simultaneously. We consider threshold models, and present a general model allowing for different regimes both in time and in levels, where regime transitions may happen according to self‐exciting, or smoothly varying or piecewise linear threshold modelling. Since fitting such a model involves the choice of a large number of structural parameters, we propose a procedure based on genetic algorithms, evaluating models by means of a generalized identification criterion. The performance of the proposed procedure is illustrated with a simulation study and applications to some real data.