Evolving Multilevel Forecast Combination Models - An Experimental Study

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Riedel:2005:NiSIS,
  author =       "Silvia Riedel and Bogdan Gabrys",
  title =        "Evolving Multilevel Forecast Combination Models - An
                 Experimental Study",
  booktitle =    "First European Symposium on Nature-inspired Smart
                 Information Systems, Workshop on Nature-inspired Data
                 Base Technology, NiSIS 2005",
  year =         "2005",
  editor =       "Derek Linkens",
  address =      "Albufeira, Portugal",
  month =        "4-5 " # oct # " 2015",
  keywords =     "genetic algorithms, genetic programming, forecast
                 combination, adaptive forecasting, genetic programming
                 airline, revenue management",
  annote =       "The Pennsylvania State University CiteSeerX Archives",
  bibsource =    "OAI-PMH server at citeseerx.ist.psu.edu",
  language =     "en",
  oai =          "oai:CiteSeerX.psu:10.1.1.484.8838",
  rights =       "Metadata may be used without restrictions as long as
                 the oai identifier remains attached to it.",
  URL =          "http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.484.8838",
  URL =          "http://www.nisis.risk-technologies.com/msc/papers/A22b_p_RiedelGabrys.pdf",
  URL =          "http://www.nisis.risk-technologies.com/msc/PortugalProgrammDraft.aspx",
  size =         "10 pages",
  abstract =     "This paper provides a description and experimental
                 comparison of different forecast combination techniques
                 for the application of Revenue Management forecasting
                 for Airlines. In order to benefit from the advantages
                 of forecasts predicting seasonal demand using different
                 forecast models on different aggregation levels and to
                 reduce the risks of high noise terms on low level
                 predictions and over generalisation on higher levels,
                 various approaches based on combination of many
                 predictions are presented and experimentally compared.
                 We propose to evolve combination structures dynamically
                 using Evolutionary Computing approaches. The evolved
                 structures are not only able to generate predictions
                 representing well balanced and stable fusions of
                 methods and levels, they are also characterised by high
                 adaptive capabilities. The focus on different levels or
                 methods of forecasting may change as well as the
                 complexity of the combination structure depending on
                 changes in parts of the input data space in different
                 data aggregation levels. Significant forecast
                 improvements have been obtained when using the proposed
                 dynamic multilevel structures.",
  notes =        "The Nature-inspired Smart Information Systems project
                 was funded by the European Commission.",
}

Genetic Programming entries for Silvia Riedel Bogdan Gabrys

Citations