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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Bibliographic Details
Published inJournal of Electrical Systems Vol. 20; no. 2; pp. 251 - 257
Main Authors Liu, Hui, He, Mingfang, Lai, Shenghan, Zhong, Xiaoying
Format Journal Article
LanguageEnglish
Published Paris Engineering and Scientific Research Groups 18.04.2024
Subjects
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
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Summary: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.
ISSN:1112-5209
DOI:10.52783/jes.1168