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Artificial Intelligence Review

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21.11.2017

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

verfasst von: Francisco Martínez, María Pilar Frías, María Dolores Pérez, Antonio Jesús Rivera

Erschienen in: Artificial Intelligence Review | Ausgabe 3/2019

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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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Titel: A methodology for applying k-nearest neighbor to time series forecasting
verfasst von: Francisco Martínez
María Pilar Frías
María Dolores Pérez
Antonio Jesús Rivera
Publikationsdatum: 21.11.2017
Verlag: Springer Netherlands
Erschienen in: Artificial Intelligence Review / Ausgabe 3/2019
Print ISSN: 0269-2821
Elektronische ISSN: 1573-7462
DOI: https://doi.org/10.1007/s10462-017-9593-z

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