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

By F. Las-Heras, Francisco Javier de Cos Juez, A. S. Sánchez, A. Krzemień, P. R. Fernández. 2015

140 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Management Science and Operations Research, Mechanical Engineering, Time-Series & Forecasting

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Omer Berat Sezer
2. A. Ozbayoglu
3. M. Ugur Gudelek

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

In Mechanical Engineering

1. A. Ozbayoglu
2. Omer Berat Sezer
3. M. Ugur Gudelek

In Time-Series & Forecasting

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “[156] Copper prices from NYMEX 2002-2014 Price data Elman RNN RMSE R”