Time series prediction evolving Voronoi regions Articles uri icon

publication date

  • February 2011

start page

  • 116

end page

  • 126

issue

  • 1

volume

  • 34

international standard serial number (ISSN)

  • 0924-669X

electronic international standard serial number (EISSN)

  • 1573-7497

abstract

  • Time series prediction is a complex problem that consists of forecasting the future behavior of a set of data with the only information of the previous data. The main problem is the fact that most of the time series that represent real phenomena include local behaviors that cannot be modelled by global approaches. This work presents a new procedure able to find predictable local behaviors, and thus, attaining a better level of total prediction. This new method is based on a division of the input space into Voronoi regions by means of Evolution Strategies. Our method has been tested using different time series domains. One of them that represents the water demand in a water tank, through a long period of time. The other two domains are well known examples of chaotic time series (Mackey-Glass) and natural phenomenon time series (Sunspot). Results prove that, in most of cases, the proposed algorithm obtain better results than other algorithms commonly used.