Evolving time series forecasting ARMA models

Evolving time series forecasting ARMA models

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Article ID: iaor20043800
Country: Netherlands
Volume: 10
Issue: 4
Start Page Number: 415
End Page Number: 429
Publication Date: Jul 2004
Journal: Journal of Heuristics
Authors: , ,
Keywords: ARIMA processes, Bayesian modelling
Abstract:

Time Series Forecasting (TSF) allows the modeling of complex systems as “black-boxes”, being a focus of attention in several research arenas such as Operational Research, Statistics or Computer Science. Alternative TSF approaches emerged from the Artificial Intelligence arena, where optimization algorithms inspired on natural selection processes, such as Evolutionary Algorithms (EAs), are popular. The present work reports on a two-level architecture, where a (meta-level) binary EA will search for the best ARMA model, being the parameters optimized by a (low-level) EA, which encodes real values. The handicap of this approach is compared with conventional forecasting methods, being competitive.

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