Construction and application of gold price prediction model based on historical data
This article delves into the methods and practices of building gold price prediction models based on historical data. Predictive models were constructed using machine learning methods such as random forests and BP neural networks by collecting and preprocessing data on gold prices and related econom...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2025-01-01
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| Series: | ITM Web of Conferences |
| Subjects: | |
| Online Access: | https://www.itm-conferences.org/articles/itmconf/pdf/2025/08/itmconf_emit2025_01037.pdf |
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| Summary: | This article delves into the methods and practices of building gold price prediction models based on historical data. Predictive models were constructed using machine learning methods such as random forests and BP neural networks by collecting and preprocessing data on gold prices and related economic indicators from September 2022 to December 2023. Trained and validated, BP neural network models excel in prediction accuracy and stability due to their strong nonlinear fitting capabilities. It can provide investors with accurate decision-making basis and help to preserve and increase the value of assets. |
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| ISSN: | 2271-2097 |