Dynamical model reconstruction and accurate prediction of power-pool time series

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@Article{VLB06,
  title =        "Dynamical model reconstruction and accurate prediction
                 of power-pool time series",
  author =       "Vinay Varadan and Henry Leung and Eloi Bosse",
  journal =      "IEEE Transactions on Instrumentation and Measurement",
  volume =       "55",
  number =       "1",
  month =        feb,
  year =         "2006",
  pages =        "327--336",
  keywords =     "genetic algorithms, genetic programming, Lyapunov
                 methods, chaos, delay estimation, fractals, least
                 squares approximations, nonlinear dynamical systems,
                 power markets, prediction theory, time series,
                 Lyapunov-dimension calculation, Lyapunov-spectrum,
                 attractor-dimension, chaos, correlation-dimension
                 calculation, delay embedding, delay estimation,
                 dynamical model reconstruction, embedding dimension,
                 fractal dimension, fractal-dimension estimates, least
                 squares genetic programming, local state-space
                 predictor, low-dimensional chaotic dynamical system,
                 nonlinear dynamics, nonlinear time-series analysis,
                 nonlinearity tests, power price, power-pool demand,
                 power-pool time series prediction, prediction analysis,
                 radial basis function neural network, stationarity
                 tests, Chaos, Lyapunov exponents, fractal dimension,
                 GP, local prediction, nonlinear time-series analysis,
                 power price and demand prediction, power-pool time
                 series, radial basis function (RBF) neural net",
  ISSN =         "0018-9456",
  DOI =          "doi:10.1109/TIM.2005.861492",
  size =         "10 pages",
  abstract =     "The emergence of the power pool as a popular
                 institution for trading of power in different countries
                 has led to increased interest in the prediction of
                 power demand and price. We investigate whether the time
                 series of power-pool demand and price can be modelled
                 as the output of a low-dimensional chaotic dynamical
                 system by using delay embedding and estimation of the
                 embedding dimension, attractor-dimension or
                 correlation-dimension calculation, Lyapunov-spectrum
                 and Lyapunov-dimension calculation, stationarity and
                 nonlinearity tests, as well as prediction analysis.
                 Different dimension estimates are consistent and show
                 close similarity, thus increasing the credibility of
                 the fractal-dimension estimates. The Lyapunov spectrum
                 consistently shows one positive Lyapunov exponent and
                 one zero exponent with the rest being negative,
                 pointing to the existence of chaos. The authors then
                 propose a least squares genetic programming (LS-GP) to
                 reconstruct the nonlinear dynamics from the power-pool
                 time series. Compared to some standard predictors
                 including the radial basis function (RBF) neural
                 network and the local state-space predictor, the
                 proposed method does not only achieve good prediction
                 of the power-pool time series but also accurately
                 predicts the peaks in the power price and demand based
                 on the data sets used in the present study.",
  notes =        "INSPEC Accession Number:8768025

                 Dept. of Electr. Eng., Columbia Univ., New York, NY,
                 USA",
}

Genetic Programming entries for Vinay Varadan Henry Leung Eloi Bosse

Citations