Forecasting the COMEX copper spot price by means of neural networks and ARIMA models

Fernando Sánchez Lasheras, Francisco Javier de Cos Juez, Ana Suárez Sánchez, Alicja Krzemień and Pedro Riesgo Fernández

Resources Policy, 2015, vol. 45, issue C, 37-43

Abstract: This paper examines the forecasting performance of ARIMA and two different kinds of artificial neural networks models (multilayer perceptron and Elman) using published data of copper spot prices from the New York Commodity Exchange, (COMEX). The empirical results obtained showed a better performance of both neural networks models over the ARIMA. The findings of this research are in line with some previous studies, which confirmed the superiority of neural networks over ARIMA models in relative research areas.

Keywords: Neural networks; Autoregressive integrated moving average (ARIMA); Time series analysis; Copper; Price forecasting; New York Commodity Exchange (COMEX) (search for similar items in EconPapers)
JEL-codes: C45 C88 Q31 (search for similar items in EconPapers)
Date: 2015
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (47)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:45:y:2015:i:c:p:37-43

DOI: 10.1016/j.resourpol.2015.03.004

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