In this article, we introduce an automatic identification procedure for transfer function models. These models are commonplace in time-series analysis, but their identification can be complex. To tackle this problem, we propose to couple a nonlinear conditional least-squares algorithm with a genetic search over the model space. We illustrate the performances of our proposal by examples on simulated and real data.

Automatic identification of seasonal transfer function models by means of iterative stepwise and genetic algorithms

CHIOGNA M;MASAROTTO G.
2008

Abstract

In this article, we introduce an automatic identification procedure for transfer function models. These models are commonplace in time-series analysis, but their identification can be complex. To tackle this problem, we propose to couple a nonlinear conditional least-squares algorithm with a genetic search over the model space. We illustrate the performances of our proposal by examples on simulated and real data.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11585/646506
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