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

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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.

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Notes

  1. http://www.neural-forecasting-competition.com/NN3/index.htm.

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Authors and Affiliations

  1. Computer Science Department, University of Jaén, Campus Las Lagunillas s/n, 23071, Jaén, Spain

    Francisco Martínez, María Dolores Pérez & Antonio Jesús Rivera

  2. Statistics and Operations Research Department, University of Jaén, Jaén, Spain

    María Pilar Frías

Authors
  1. Francisco Martínez
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  2. María Pilar Frías
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  3. María Dolores Pérez
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  4. Antonio Jesús Rivera
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Corresponding author

Correspondence to Francisco Martínez.

Additional information

This paper has been partially supported by the project TIN2015-68854-R (FEDER Founds) of the Spanish Ministry of Economy and Competitiveness.

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Martínez, F., Frías, M.P., Pérez, M.D. et al. A methodology for applying k-nearest neighbor to time series forecasting. Artif Intell Rev 52, 2019–2037 (2019). https://doi.org/10.1007/s10462-017-9593-z

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