Learning patterns of states from multi-channel time series using genetic programming

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

  author =       "Andy Song and Feng Xie and Vic Ciesielski",
  title =        "Learning patterns of states from multi-channel time
                 series using genetic programming",
  journal =      "Soft Computing",
  year =         "2016",
  volume =       "20",
  number =       "10",
  pages =        "3915--3925",
  keywords =     "genetic algorithms, genetic programming, Pattern
                 learning, Time series, States, Multi-channel time
  ISSN =         "1433-7479",
  DOI =          "doi:10.1007/s00500-016-2127-9",
  size =         "11 pages",
  abstract =     "A state in time series is time series data stream
                 maintaining a certain pattern over a period of time,
                 for example, holding a steady value, being above a
                 certain threshold and oscillating regularly. Automatic
                 learning and discovery of these patterns of time series
                 states can be useful in a range of scenarios of
                 monitoring and classifying stream data, for example,
                 activity recognition based on body sensor readings. In
                 this study, we present our genetic programming
                 (GP)-based time series analysis method on learning
                 various types of states from multi-channel data
                 streams. This evolutionary learning method can handle
                 relatively complex scenarios using only raw input. This
                 method does not require prior knowledge of the
                 relationships between channels. It does not require
                 manually defined feature to be constructed. The
                 evaluation using both artificial and real-world
                 multi-channel time series data shows that this method
                 on raw input can outperform classic learning methods on
                 pre-defined features. The analysis shows patterns can
                 be discovered by the GP method.",

Genetic Programming entries for Andy Song Feng Xie Victor Ciesielski