Time series modeling and forecasting using memetic algorithms for regime-switching models

Christoph Bergmeir, Isaac Triguero, Daniel Molina, José Luis Aznarte, José Manuel Benitez

Research output: Contribution to journalArticleResearchpeer-review

12 Citations (Scopus)

Abstract

In this brief, we present a novel model fitting procedure for the neuro-coefficient smooth transition autoregressive model (NCSTAR), as presented by Medeiros and Veiga. The model is endowed with a statistically founded iterative building procedure and can be interpreted in terms of fuzzy rule-based systems. The interpretability of the generated models and a mathematically sound building procedure are two very important properties of forecasting models. The model fitting procedure employed by the original NCSTAR is a combination of initial parameter estimation by a grid search procedure with a traditional local search algorithm. We propose a different fitting procedure, using a memetic algorithm, in order to obtain more accurate models. An empirical evaluation of the method is performed, applying it to various real-world time series originating from three forecasting competitions. The results indicate that we can significantly enhance the accuracy of the models, making them competitive to models commonly used in the field.

Original languageEnglish
Article number6323088
Pages (from-to)1841-1847
Number of pages7
JournalIEEE Transactions on Neural Networks and Learning Systems
Volume23
Issue number11
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Autoregression
  • memetic algorithms
  • neuro-coefficient smooth transition autoregressive model (NCSTAR)
  • regime-switching models
  • threshold autoregressive model (TAR)

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