A New Machine Learning Forecasting Algorithm Based on Bivariate Copula Functions

J. A. Carrillo, M. Nieto, J. F. Velez and D. Velez
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J. A. Carrillo: Department of Statistics and Operational Research, Faculty of Mathematics, Complutense University of Madrid, 28040 Madrid, Spain
M. Nieto: Department of Statistics and Operational Research, Faculty of Mathematics, Complutense University of Madrid, 28040 Madrid, Spain
J. F. Velez: Department of Computer Science, Escuela Tecnica Superior de Ingenieria Informatica, Universidad Rey Juan Carlos, Mostoles, 28933 Madrid, Spain
D. Velez: Department of Statistics and Operational Research, Faculty of Mathematics, Complutense University of Madrid, 28040 Madrid, Spain

Forecasting, 2021, vol. 3, issue 2, 1-22

Abstract: A novel forecasting method based on copula functions is proposed. It consists of an iterative algorithm in which a dependent variable is decomposed as a sum of error terms, where each one of them is estimated identifying the input variable which best “copulate” with it. The method has been tested over popular reference datasets, achieving competitive results in comparison with other well-known machine learning techniques.

Keywords: copula; machine learning; conditional probabilistic forecast (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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