Genetic programming model for forecast of short and noisy data

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@Article{Sivapragasam2007266,

• author = "C. Sivapragasam and P. Vincent and G. Vasudevan",
• title = "Genetic programming model for forecast of short and noisy data",
• journal = "Hydrological Processes",
• year = "2007",
• volume = "21",
• number = "2",
• pages = "266--272",
• month = "15 " # jan,
• keywords = "genetic algorithms, genetic programming, Forecasting, Mathematical models, Random processes, Rivers, Time series analysis, Flow forecasting, Genetic programming, Noise filtering, Flow of water, Flow of water, Mathematical models, Random processes, Rivers, Time series analysis, artificial neural network, forecasting method, model, noise, river flow, Artificial neural networks",
• ISSN = "1099-1085",
• URL = "http://onlinelibrary.wiley.com/doi/10.1002/hyp.6226/abstract",
• DOI = "doi:10.1002/hyp.6226",
• size = "7 pages",
• abstract = "Though forecasting of river flow has received a great deal of attention from engineers and researchers throughout the world, this still continues to be a challenging task owing to the complexity of the process. In the last decade or so, artificial neural networks (ANNs) have been widely applied, and their ability to model complex phenomena has been clearly demonstrated. However, the success of ANNs depends very crucially on having representative records of sufficient length. Further, the forecast accuracy decreases rapidly with an increase in the forecast horizon. In this study, the use of the Darwinian theory-based recent evolutionary technique of genetic programming (GP) is suggested to forecast fortnightly flow up to 4-lead. It is demonstrated that short lead predictions can be significantly improved from a short and noisy time series if the stochastic (noise) component is appropriately filtered out. The deterministic component can then be easily modelled. Further, only the immediate antecedent exogenous and/or non-exogenous inputs can be assumed to control the process. With an increase in the forecast horizon, the stochastic components also play an important role in the forecast, besides the inherent difficulty in ascertaining the appropriate input variables which can be assumed to govern the underlying process. GP is found to be an efficient tool to identify the most appropriate input variables to achieve reasonable prediction accuracy for higher lead-period forecasts. A comparison with ANNs suggests that though there is no significant difference in the prediction accuracy, GP does offer some unique advantages.",
• affiliation = "Department of Civil Engineering, Mepco Schlenk Engineering College, Sivakasi 626005 Tamilnadu State, India",
• correspondence_address1 = "Sivapragasam, C.; Department of Civil Engineering, Mepco Schlenk Engineering College, Sivakasi 626005 Tamilnadu State, India; email: sivapragasam@yahoo.com",
• language = "English",
• document_type = "Article",

}

Genetic Programming entries for C Sivapragasam P Vincent G Vasudevan

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