A methodology for coffee price forecasting based on extreme learning machines-Reference-Cited by-同舟云学术

A methodology for coffee price forecasting based on extreme learning machines

Published:2021-07 Issue: Volume: Page:
ISSN:2214-3173
Container-title:Information Processing in Agriculture
language:en
Short-container-title:Information Processing in Agriculture

Author:

Deina Carolina,do Amaral Prates Matheus Henrique,Alves Carlos Henrique Rodrigues,Martins Marcella Scoczynski Ribeiro,Trojan Flavio,Stevan Sergio Luiz,Siqueira Hugo Valadares

Funder

National Council for Scientific and Technological Development

Araucaria Foundation

Publisher

Elsevier BV

Subject

Computer Science Applications,Agronomy and Crop Science,Animal Science and Zoology,Aquatic Science,Forestry

Reference47 articles.

1. International Coffee Organization. Coffee Market Report. URL: http://www.ico.org. 2020.

2. A comparison of various forecasting techniques for coffee prices;Novanda;J Phys Conf Ser,2018

3. Measuring the functional efficiency ofagricultural futures markets;Consuegra;Aust J Agric Resour Econ,2017

4. Fundamentals, speculation or macroeconomic conditions? Modeling and forecasting Arabica coffee prices;Cuaresma;European Review of Agricultural Economics,2018

5. Optimization of combined time series methods to forecast the demand for coffee in Brazil: A new approach using Normal Boundary Intersection coupled with mixture designs of experiments and rotated factor scores;Bacci;Int J Prod Econ,2019

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