Genetic programming model for forecast of short and noisy data

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@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,
                 Genetic algorithms, Mathematical models, Random
                 processes, Rivers, Time series analysis, Flow
                 forecasting, Genetic programming, Noise filtering, Flow
                 of water, Flow of water, Forecasting, Genetic
                 algorithms, 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

Citations