Time series analysis for COMEX platinum spot price forecasting using SVM, MARS, MLP, VARMA and ARIMA models: A case study

Luis Alfonso Menéndez-García, Paulino José García-Nieto, Esperanza García-Gonzalo and Fernando Sánchez Lasheras

Resources Policy, 2024, vol. 95, issue C

Abstract: This article looks at predicting the price of platinum, along with 12 other commodity prices, using both time series and machine learning models. Platinum, characterised by its rarity and significant industrial and artistic value, occupies a unique position among chemical elements. This research contributes to econometrics by showing the effectiveness of advanced modelling techniques in predicting precious metal prices, providing valuable insights for the field. Platinum prices are volatile, yet they serve as a crucial indicator of the global economy. Fluctuations in the platinum price can signal increased global growth or an impending economic downturn. The study focuses on the forecasting of platinum spot prices from the New York Commodity Exchange, using various time series machine learning models (MARS, SVM and MLP) as well as classical techniques (ARIMA and VARMA). In particular, the Artificial Neural Network (MLP) model emerges as the best performing model, with the highest predictive accuracy and a Root Mean Square Error (RMSE) of 9.24. The ARIMA time series model, on the other hand, is the worst performer with an RMSE of 74.94. The superior accuracy of the MLP method underlines its ability to identify complex relationships between platinum and other commodities. This research highlights the potential of machine learning techniques, particularly MLP, in accurately forecasting the price of platinum, with benefits for investors, industry professionals and policymakers alike.

Keywords: Time series platinum price forecast; Multivariate adaptive regression splines (MARS); Support vector machines (SVMs); Artificial neural networks (ANNs); Vector autoregressive moving-average (VARMA); Autoregressive integrated moving-average (ARIMA) (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:95:y:2024:i:c:s0301420724005154

DOI: 10.1016/j.resourpol.2024.105148

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