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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| Published in: | Communications in statistics. Simulation and computation Vol. 54; no. 8; pp. 2959 - 2973 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Philadelphia
Taylor & Francis
03.08.2025
Taylor & Francis Ltd |
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| ISSN: | 0361-0918, 1532-4141 |
| Online Access: | Get full text |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Xu, Xiaojie Zhang, Yun |
| Author_xml | – sequence: 1 givenname: Xiaojie surname: Xu fullname: Xu, Xiaojie organization: NC State University – sequence: 2 givenname: Yun surname: Zhang fullname: Zhang, Yun organization: NC State University |
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| SubjectTerms | Commodities Forecasting Machine learning Neural network Neural networks Palladium Platinum Policy analysis Price forecasting Pricing Time series |
| Title | Platinum and palladium price forecasting through neural networks |
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