Assessing Performance of the Transformer Model in Predicting Hog Prices
The price of hogs has a significant impact on livelihoods, social development, and overall stability. Therefore, accurate prediction of hog prices is crucial for effective decision-making, breeding strategies, resource allocation, and risk mitigation. In this study, we compare the performance of Tra...
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Published in | Journal of Electrical Systems Vol. 20; no. 2; pp. 251 - 257 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Paris
Engineering and Scientific Research Groups
18.04.2024
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Abstract | The price of hogs has a significant impact on livelihoods, social development, and overall stability. Therefore, accurate prediction of hog prices is crucial for effective decision-making, breeding strategies, resource allocation, and risk mitigation. In this study, we compare the performance of Transformer and Recurrent Neural Network (RNN) models in predicting hog prices and evaluate their applicability in different scenarios. Additionally, we conduct a generalization test on the hog pig industry chain to assess the models' performance. Our findings indicate that Transformer models excel in parallel computing, context capture, and encoding/decoding tasks. On the other hand, RNN models demonstrate superior performance in predicting extreme events and localized tasks. Therefore, the choice of modeling method should be tailored to meet specific requirements based on the nature of the prediction task. |
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AbstractList | The price of hogs has a significant impact on livelihoods, social development, and overall stability. Therefore, accurate prediction of hog prices is crucial for effective decision-making, breeding strategies, resource allocation, and risk mitigation. In this study, we compare the performance of Transformer and Recurrent Neural Network (RNN) models in predicting hog prices and evaluate their applicability in different scenarios. Additionally, we conduct a generalization test on the hog pig industry chain to assess the models' performance. Our findings indicate that Transformer models excel in parallel computing, context capture, and encoding/decoding tasks. On the other hand, RNN models demonstrate superior performance in predicting extreme events and localized tasks. Therefore, the choice of modeling method should be tailored to meet specific requirements based on the nature of the prediction task. |
Author | Liu, Hui Lai, Shenghan Zhong, Xiaoying He, Mingfang |
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ContentType | Journal Article |
Copyright | 2024. This work is published under https://creativecommons.org/licenses/by/4.0/legalcode (the“License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
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SubjectTerms | Algorithms Deep learning Distance learning Econometrics Encoding-Decoding Hogs Machine learning Natural language processing Neural networks Performance prediction Pork Prices Recurrent neural networks Resource allocation Risk allocation Society Transformers |
Title | Assessing Performance of the Transformer Model in Predicting Hog Prices |
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