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

TitleA methodology for applying k-nearest neighbor to time series forecasting
Publication TypeJournal Article
Year of Publication2017
AuthorsMartínez, Francisco, Frías María Pilar, Pérez María Dolores, and Rivera Antonio Jesús
JournalArtificial Intelligence Review
Date PublishedNov
ISSN1573-7462
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.

URLhttps://doi.org/10.1007/s10462-017-9593-z
DOI10.1007/s10462-017-9593-z