Using transformer to predict the price of commodity

In this study, we explored the use of the Transformer model for time serial prediction and applied it to commodity price prediction, particularly for specific commodities and gold prices in the market. Given the potential of deep learning in processing large-scale datasets and capturing complex nonl...

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Main Author: Zhou, Siyu
Other Authors: Wang Lipo
Format: Thesis-Master by Coursework
Language:English
Published: Nanyang Technological University 2024
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Online Access:https://hdl.handle.net/10356/177390
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1773902024-05-24T15:56:25Z Using transformer to predict the price of commodity Zhou, Siyu Wang Lipo School of Electrical and Electronic Engineering ELPWang@ntu.edu.sg Computer and Information Science Transformer In this study, we explored the use of the Transformer model for time serial prediction and applied it to commodity price prediction, particularly for specific commodities and gold prices in the market. Given the potential of deep learning in processing large-scale datasets and capturing complex nonlinear patterns, this study aims to explore the application effect of Transformer models in financial time series prediction. Firstly, we reviewed the relevant work in the field of time series prediction, particularly the application of deep learning methods such as recurrent neural networks (RNN), long short-term memory networks (LSTM), and Transformer models. Subsequently, this study improved the standard Transformer model to better adapt to the demand for time series prediction, especially in commodity price prediction. This dissertation first imitates the relevant research of Dr. Hachmi Ben Ameur and predicts the Bloomberg Commodity Index and its component indices: Bloomberg Industrial Metals and Bloomberg Precious Metals. Based on this, the price of gold in the market is predicted. The experimental results show that the model has achieved a high level of prediction accuracy, proving the effectiveness of the Transformer model in such tasks. Specifically, the prediction case of gold prices further validates the robustness and adaptability of the model in handling time series data of different types of commodity prices. This study not only demonstrates the potential of deep learning, especially Transformer models, in financial time series prediction but also provides valuable insights and potential methodological guidance for future research in similar fields. Master's degree 2024-05-24T12:54:43Z 2024-05-24T12:54:43Z 2024 Thesis-Master by Coursework Zhou, S. (2024). Using transformer to predict the price of commodity. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177390 https://hdl.handle.net/10356/177390 en application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Transformer
spellingShingle Computer and Information Science
Transformer
Zhou, Siyu
Using transformer to predict the price of commodity
description In this study, we explored the use of the Transformer model for time serial prediction and applied it to commodity price prediction, particularly for specific commodities and gold prices in the market. Given the potential of deep learning in processing large-scale datasets and capturing complex nonlinear patterns, this study aims to explore the application effect of Transformer models in financial time series prediction. Firstly, we reviewed the relevant work in the field of time series prediction, particularly the application of deep learning methods such as recurrent neural networks (RNN), long short-term memory networks (LSTM), and Transformer models. Subsequently, this study improved the standard Transformer model to better adapt to the demand for time series prediction, especially in commodity price prediction. This dissertation first imitates the relevant research of Dr. Hachmi Ben Ameur and predicts the Bloomberg Commodity Index and its component indices: Bloomberg Industrial Metals and Bloomberg Precious Metals. Based on this, the price of gold in the market is predicted. The experimental results show that the model has achieved a high level of prediction accuracy, proving the effectiveness of the Transformer model in such tasks. Specifically, the prediction case of gold prices further validates the robustness and adaptability of the model in handling time series data of different types of commodity prices. This study not only demonstrates the potential of deep learning, especially Transformer models, in financial time series prediction but also provides valuable insights and potential methodological guidance for future research in similar fields.
author2 Wang Lipo
author_facet Wang Lipo
Zhou, Siyu
format Thesis-Master by Coursework
author Zhou, Siyu
author_sort Zhou, Siyu
title Using transformer to predict the price of commodity
title_short Using transformer to predict the price of commodity
title_full Using transformer to predict the price of commodity
title_fullStr Using transformer to predict the price of commodity
title_full_unstemmed Using transformer to predict the price of commodity
title_sort using transformer to predict the price of commodity
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/177390
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