A New Machine Learning Forecasting Algorithm Based on Bivariate Copula Functions

A New Machine Learning Forecasting Algorithm Based on Bivariate Copula Functions

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 ov...

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Veröffentlicht in:Forecasting 2021-06, Vol.3 (2), p.355-376
Hauptverfasser: Carrillo, J. A., Nieto, M., Velez, J. F., Velez, D.
Format: Artikel
Sprache:eng
Schlagworte:
Algorithms
Capital assets
conditional probabilistic forecast
copula
Datasets
Expected values
Forecasting
Machine learning
Neural networks
Simulation
Stochastic models
Time series
Variables
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Zusammenfassung: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.
ISSN:2571-9394
2571-9394
DOI:10.3390/forecast3020023