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A methodology for applying k-nearest neighbor to time series forecasting

  • Francisco Martínez
  • María Pilar Frías
  • María Dolores Pérez
  • Antonio Jesús Rivera
Article

Abstract

In this paper a methodology for applying k-nearest neighbor regression on a time series forecasting context is developed. The goal is to devise an automatic tool, i.e., a tool that can work without human intervention; furthermore, the methodology should be effective and efficient, so that it can be applied to accurately forecast a great number of time series. In order to be incorporated into our methodology, several modeling and preprocessing techniques are analyzed and assessed using the N3 competition data set. One interesting feature of the proposed methodology is that it resolves the selection of important modeling parameters, such as k or the input variables, combining several models with different parameters. In spite of the simplicity of k-NN regression, our methodology seems to be quite effective.

Keywords

Nearest neighbors Time series forecasting Combined forecast Feature selection 

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Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2017

Authors and Affiliations

  1. 1.Computer Science DepartmentUniversity of JaénJaénSpain
  2. 2.Statistics and Operations Research DepartmentUniversity of JaénJaénSpain

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