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

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...

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Bibliographic Details
Published in:The Artificial intelligence review 2019-10, Vol.52 (3), p.2019-2037
Main Authors: Martínez, Francisco, Frías, María Pilar, Pérez, María Dolores, Rivera, Antonio Jesús
Format: Article
Language:English
Subjects:
Artificial Intelligence
Computer Science
Forecasting
Mathematical models
Methodology
Modelling
Preprocessing
Regression analysis
Time series
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Summary: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.
ISSN:0269-2821
1573-7462
DOI:10.1007/s10462-017-9593-z