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A Deep Learning Framework for Forecasting Pork Import Prices Using PPI_IPD Index

Published: 26 December 2024 Publication History

Abstract

As we all know, pork prices are difficult to predict because of the complex and changeable market. In order to meet this challenge, STL-LSTM model is introduced in this study, which combines STL preprocessing method with LSTM network. By introducing PPI_IPD as the input feature, STL-LSTM model uses several selected variables to improve the accuracy of pork price forecast. The experimental results show that this method is superior to the traditional univariate forecasting method, which marks a significant progress in pork price forecasting research.

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ICEA '23: Proceedings of the 2023 International Conference on Intelligent Computing and Its Emerging Applications
December 2023
175 pages
ISBN:9798400709050
DOI:10.1145/3659154
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 26 December 2024

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Author Tags

  1. LSTM
  2. PPI_IPD
  3. Pork price forecasting.

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