基于VMD-BO-BiLSTM的猪肉价格预测模型

doi:10.3969/j.issn.0255-8297.2023.04.013

摘要/Abstract

摘要： 基于猪肉价格的非线性与波动性特性，提出一种基于变分模态分解（variationalmodal decomposition,VMD）和贝叶斯优化（Bayesian optimization,BO）的双向长短时记忆（bidirectional long short-term memory,BiLSTM）网络的猪肉价格预测方法。首先采用变分模态分解对数据进行预处理，将数据分解为具有相对简单波动的子序列；然后通过贝叶斯算法对双向长短时记忆网络模型的第1、2隐含层神经元数目、学习率和批次大小进行寻优，根据寻优的结果建立预测模型。实验结果表明： VMD-BO-BiLSTM方法的平均绝对误差、均方根误差、平均绝对百分比误差和确定系数分别为1.101 214、1.466 100、0.040 631、0.987 760，相比传统单一的LSTM，BiLSTM模型精确度更高，有更高的适用性，适合对猪肉价格预测。

关键词: 双向长短时记忆, 贝叶斯, 猪肉价格预测, 变分模态分解, 超参数

Abstract: Based on the nonlinear and fluctuating characteristics of pork price, this paper proposes a pork price prediction approach using variational modal decomposition (VMD) and Bayesian optimization-based bidirectional long short-term memory (BiLSTM). VMD decomposes the data into subsequences with simple fluctuations, which are then used in BiLSTM. Bayesian optimization is adopted to optimize the number of neurons, learning rate, and batch size of the first and second hidden layers of the BiLSTM network model. Experimental results show that the proposed VMD-BO-BiLSTM method outperforms traditional single LSTM and BiLSTM models in terms of mean absolute error, root mean square error, mean absolute percentage error, and determination coefficient. It has higher accuracy and applicability for pork price prediction.

Key words: bidirectional long short-term memory (BiLSTM), Bayesian, pork price forecast, variational modal decomposition, hyper-paramete

中图分类号:

TP391

胡春安, 江维. 基于VMD-BO-BiLSTM的猪肉价格预测模型[J]. 应用科学学报, 2023, 41(4): 692-704.

HU Chun'an, JIANG Wei. Pork Price Prediction Model Based on VMD-BO-BILSTM[J]. Journal of Applied Sciences, 2023, 41(4): 692-704.

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