Multi-step Ahead Forecasting Using Cartesian Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Ivars Dzalbs and Tatiana Kalganova",
  title =        "Multi-step Ahead Forecasting Using Cartesian Genetic
  booktitle =    "Inspired by Nature: Essays Presented to Julian F.
                 Miller on the Occasion of his 60th Birthday",
  publisher =    "Springer",
  year =         "2017",
  editor =       "Susan Stepney and Andrew Adamatzky",
  volume =       "28",
  series =       "Emergence, Complexity and Computation",
  chapter =      "11",
  pages =        "235--246",
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming",
  isbn13 =       "978-3-319-67996-9",
  DOI =          "doi:10.1007/978-3-319-67997-6_11",
  abstract =     "This paper describes a forecasting method that is
                 suitable for long range predictions. Forecasts are made
                 by a calculating machine of which inputs are the actual
                 data and the outputs are the forecasted values. The
                 Cartesian Genetic Programming (CGP) algorithm finds the
                 best performing machine out of a huge abundance of
                 candidates via evolutionary strategy. The algorithm can
                 cope with non-stationary highly multivariate data
                 series, and can reveal hidden relationships among the
                 input variables. Multiple experiments were devised by
                 looking at several time series from different
                 industries. Forecast results were analysed and compared
                 using average Symmetric Mean Absolute Percentage Error
                 (SMAPE) across all datasets. Overall, CGP achieved
                 comparable to Support Vector Machine algorithm and
                 performed better than Neural Networks.",
  notes =        "part of \cite{miller60book}

Genetic Programming entries for Ivars Dzalbs Tatiana Kalganova