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AVERAGE MONTHLY RAINFALL FORECAST IN ROMANIA BY USING k-NEAREST NEIGHBORS REGRESSION

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  • MARINOIU CRISTIAN

    (PETROLEUM-GAS UNIVERSITY OF PLOIESTI)

Abstract

The discovery of the best strategies for achieving future values forecast of a time series represents a permanent concern in time series analysis, highly motivated from a theoretical point of view, but especially from a practical point of view. In the context of the explosive growth of machine learning techniques, their usein time series forecast is a natural step to find modern alternatives to overcome existing limitations of traditional techniques. Although it is a relatively a simple method of learning, knn (k-nearest neighbor) regression seems to be a good competitor to traditional methods. The purpose of this paper is to describe how to use this method for forecasting time series and for achieving Monthly Average Rainfall (AMR) forecast in Romania.

Suggested Citation

  • Marinoiu Cristian, 2018. "AVERAGE MONTHLY RAINFALL FORECAST IN ROMANIA BY USING k-NEAREST NEIGHBORS REGRESSION," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 4, pages 5-12, August.
    • Handle: RePEc:cbu:jrnlec:y:2018:v:4:p:5-12
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    File URL: http://www.utgjiu.ro/revista/ec/pdf/2018-04/01_marinoiu.pdf
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    1. Gianluca Bontempi & Souhaib Ben Taieb & Yann-Aël Le Borgne, 2013. "Machine learning strategies for time series forecasting," ULB Institutional Repository 2013/167761, ULB -- Universite Libre de Bruxelles.
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