Platinum and palladium price forecasting through neural networks

To many commodity market participants, forecasts of price series represent a critical task. In this work, nonlinear autoregressive neural network models' potential is explored for forecasting daily prices series of platinum and palladium over about a fifty-year period. For this purpose, one hun...

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Vydáno v:Communications in statistics. Simulation and computation Ročník 54; číslo 8; s. 2959 - 2973
Hlavní autoři: Xu, Xiaojie, Zhang, Yun
Médium: Journal Article
Jazyk:angličtina
Vydáno: Philadelphia Taylor & Francis 03.08.2025
Taylor & Francis Ltd
Témata:
Commodities
Forecasting
Machine learning
Neural network
Neural networks
Palladium
Platinum
Policy analysis
Price forecasting
Pricing
Time series
ISSN:0361-0918, 1532-4141
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Shrnutí:To many commodity market participants, forecasts of price series represent a critical task. In this work, nonlinear autoregressive neural network models' potential is explored for forecasting daily prices series of platinum and palladium over about a fifty-year period. For this purpose, one hundred and twenty model settings are examined, including different training algorithms, numbers of hidden neurons and delays, and ratios used to segment the data. With the analysis, two models leading to stable and accurate forecast results are constructed for the prices of the two commodities. In particular, the models' performance in terms of the relative root mean square error is 1.86% and 3.61% for platinum and palladium, respectively, for the overall sample. Results in this work could help technical forecasts and policy analysis. The forecast framework might be extended to other different commodities.
Bibliografie:ObjectType-Article-1
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ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2024.2330700