Time Series Pattern Recognition via SoftComputing

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Martin Kotyrba and Zuzana Oplatkova and Eva Volna and 
                 Roman Senkerik and Vaclav Kocian and Michal Janosek",
  title =        "Time Series Pattern Recognition via SoftComputing",
  booktitle =    "International Conference on P2P, Parallel, Grid, Cloud
                 and Internet Computing (3PGCIC 2011)",
  year =         "2011",
  month =        "26-28 " # oct,
  pages =        "384--389",
  address =      "Barcelona",
  size =         "6 pages",
  abstract =     "In this paper we develop two methods that are able to
                 analyse and recognise patterns in time series. The
                 first model is based on analytic programming (AP),
                 which belongs to soft computing. AP is based as well as
                 genetic programming on the set of functions, operators
                 and so-called terminals, which are usually constants or
                 independent variables. The second one uses an
                 artificial neural network that is adapted by back
                 propagation. Artificial neural networks are suitable
                 for pattern recognition in time series mainly because
                 of learning only from examples. There is no need to add
                 additional information that could bring more confusion
                 than recognition effect. Neural networks are able to
                 generalise and are resistant to noise. On the other
                 hand, it is generally not possible to determine exactly
                 what a neural network learnt and it is also hard to
                 estimate possible recognition error. They are ideal
                 especially when we do not have any other description of
                 the observed series. This paper also includes
                 experimental results of time series pattern recognition
                 carried out with both mentioned methods, which have
                 proven their suitability for this type of problem
  keywords =     "genetic algorithms, genetic programming, analytic
                 programming, artificial neural network, pattern
                 recognition, problem solving, soft computing, time
                 series, neural nets, pattern recognition, problem
                 solving, time series",
  DOI =          "doi:10.1109/3PGCIC.2011.72",
  notes =        "Also known as \cite{6154911}",

Genetic Programming entries for Martin Kotyrba Zuzana Oplatkova Eva Volna Roman Senkerik Vaclav Kocian Michal Janosek