Combining Diffusion Models and Macroeconomic Indicators with a Modified Genetic Programming Method: Implementation in Forecasting the Number of Mobile Telecommunications Subscribers in OECD Countries

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@Article{journals/advor/SalpasaranisSK14,

• author = "Konstantinos Salpasaranis and Vasilios Stylianakis and Stavros Kotsopoulos",
• title = "Combining Diffusion Models and Macroeconomic Indicators with a Modified Genetic Programming Method: Implementation in Forecasting the Number of Mobile Telecommunications Subscribers in {OECD} Countries",
• journal = "Advances Operations Research",
• year = "2014",
• volume = "2014",
• pages = "Article ID 568478",
• keywords = "genetic algorithms, genetic programming",
• bibdate = "2014-07-08",
• bibsource = "DBLP, http://dblp.uni-trier.de/db/journals/advor/advor2014.html#SalpasaranisSK14",
• URL = "http://downloads.hindawi.com/journals/aor/2014/568478.pdf",
• DOI = "doi:10.1155/2014/568478",
• size = "20 pages",
• abstract = "This paper proposes a modified Genetic Programming method for forecasting the mobile telecommunications subscribers' population. The method constitutes an expansion of the hybrid Genetic Programming (hGP) method improved by the introduction of diffusion models for technological forecasting purposes in the initial population, such as the Logistic, Gompertz, and Bass, as well as the Bi-Logistic and LogInLog. In addition, the aforementioned functions and models expand the function set of hGP. The application of the method in combination with macroeconomic indicators such as Gross Domestic Product per Capita (GDPpC) and Consumer Prices Index (CPI) leads to the creation of forecasting models and scenarios for medium- and long-term level of predictability. The forecasting module of the program has also been improved with the multi-levelled use of the statistical indices as fitness functions and model selection indices. The implementation of the modified-hGP in the datasets of mobile subscribers in the Organisation for Economic Cooperation and Development (OECD) countries shows very satisfactory forecasting performance.",

}

Genetic Programming entries for Konstantinos Salpasaranis Vasilis Stilianakis Stavros Kotsopoulos

Citations