Time Series Modeling with Genetic Programming Relative to ARIMA Models

Created by W.Langdon from gp-bibliography.bib Revision:1.7630

@InProceedings{klucik:2009:NTTS,

• author = "Miroslav Klucik and Jana Juriova and Marian Klucik",
• title = "Time Series Modeling with Genetic Programming Relative to ARIMA Models",
• booktitle = "New Techniques and Technologies in Statistics, NTTS 2009",
• year = "2009",
• address = "Brussels, Belgium",
• month = "18-20 " # feb,
• organisation = "EUROSTAT and European Commission",
• keywords = "genetic algorithms, genetic programming, symbolic regression, ARIMA",
• URL = "http://epp.eurostat.ec.europa.eu/portal/page/portal/research_methodology/documents/POSTER_4P_TIME_SERIES_MODELLING_KLUCIK.pdf",
• size = "10 pages",
• abstract = "INFOSTAT, the research institution of the Statistical Office of the Slovak Republic, is intending to supplement its model tools (ECM, ARIMA) with modern heuristic methods for analyzing and forecasting the macroeconomic reality. The initial research is concentrated on time series modelling using genetic programming and comparing the results with a more conventional ARIMA model. Genetic programming tool based on evolutionary computation technique can find not only optimal parameters of a searched function but also its structure. Our experiments deal with modeling and forecasting of the industrial production for Slovakia and European Monetary Union. For our purpose the genetic programming tool is kept as simple as possible. The predicted variables are estimated by the concept of symbolic regression. The solutions of symbolic regression are expressed in a tree-type structure. Concerning the ARIMA approach, we have used seasonal ARIMA models that satisfied all the quality model conditions. Both methods' performance was tested in a twelve-month forecasting. The second experiment involves the simulation of shocks for each model. The GP model manages to compete with ARIMA models in all cases. Finally we show a way to depict a complicated nonlinear solution in a simply understandable form. The continually changing and hardly predictable environment of contemporary and future global economy will require a multidisciplinary approach to approximate the complex reality. The GP instrument with its flexibility and efficiency manages to confront these challenges with promising results.",

}

Genetic Programming entries for Miroslav Klucik Jana Juriova Marian Klucik

Citations