Journal
ARTIFICIAL INTELLIGENCE REVIEW
Volume 52, Issue 3, Pages 2019-2037Publisher
SPRINGER
DOI: 10.1007/s10462-017-9593-z
Keywords
Nearest neighbors; Time series forecasting; Combined forecast; Feature selection
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Funding
- FEDER Founds of the Spanish Ministry of Economy and Competitiveness [TIN2015-68854-R]
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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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