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。
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.
[1] Oh S H, See M T. Pork preference for consumers in China, Japan and South Korea[J]. AsianAustralasian Journal of Animal Sciences, 2012, 25(1):143-150.
[2] Pan Z Z, Meng Z, Chen Z J, et al. A two-stage method based on extreme learning machine for predicting the remaining useful life of rolling-element bearings[J]. Mechanical Systems and Signal Processing, 2020, 144:106899.
[3] 林友芳, 蒋鹏, 郭晟楠, 等. 基于卷积神经网络的机票低价预测[J]. 北京交通大学学报, 2019, 43(5):1-9. Lin Y F, Jiang P, Guo S N, et al. Air ticket low-price prediction based on convolution neural network[J]. Journal of Beijing Jiaotong University, 2019, 43(5):1-9. (in Chinese)
[4] Zhao Z, Chen W H, Wu X M, et al. LSTM network:a deep learning approach for short-term traffic forecast[J]. IET Intelligent Transport Systems, 2017, 11(2):68-75.
[5] Shahid F, Zameer A, Muneeb M. Predictions for COVID-19 with deep learning models of LSTM, GRU and Bi-LSTM[J]. Chaos, Solitons & Fractals, 2020, 140:110212.
[6] 谢小瑜, 周俊煌, 张勇军, 等. 基于W-BiLSTM的可再生能源超短期发电功率预测方法[J]. 电力系统自动化, 2021, 45(8):175-184. Xie X Y, Zhou J H, Zhang Y J, et al. W-BiLSTM based ultra-short-term generation power prediction method of renewable energy[J]. Automation of Electric Power Systems, 2021, 45(8):175-184. (in Chinese)
[7] 王渝红, 史云翔, 周旭, 等. 基于时间模式注意力机制的BiLSTM多风电机组超短期功率预测[J]. 高电压技术, 2022, 48(5):1884-1892. Wang Y H, Shi Y X, Zhou X, et al. Ultra-short-term power prediction for BiLSTM multi wind turbines based on temporal pattern attention[J]. High Voltage Engineering, 2022, 48(5):1884-1892. (in Chinese)
[8] 赵志宏, 李晴, 杨绍普, 等. 基于BiLSTM与注意力机制的剩余使用寿命预测研究[J]. 振动与冲击, 2022, 41(6):44-50, 196. Zhao Z H, Li Q, Yang S P, et al. Remaining useful life prediction based on BiLSTM and attention mechanism[J]. Journal of Vibration and Shock, 2022, 41(6):44-50, 196. (in Chinese)
[9] Wang D Y, Luo H Y, Grunder O, et al. Multi-step ahead electricity price forecasting using a hybrid model based on two-layer decomposition technique and BP neural network optimized by firefly algorithm[J]. Applied Energy, 2017, 19(1):390-407.
[10] Altan A, Karasu S, Zio E. A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer[J]. Applied Soft Computing, 2021, 100(4):106996.
[11] Liu H, Mi X W, Li Y F. Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM[J]. Energy Conversion and Management, 2018, 159(1):54-64.
[12] 梁正刚, 李啸骢. VMD算法效果及特性分析[J]. 电气开关, 2019, 57(6):39-41. Liang Z G, Li X C. Effects and characteristics of VMD algorithm[J]. Electric Switcher, 2019, 57(6):39-41. (in Chinese)
[13] 崔佳旭, 杨博. 贝叶斯优化方法和应用综述[J]. 软件学报, 2018, 29(10):3068-3090. Cui J X, Yang B. Survey on Bayesian optimization methodology and applications[J]. Journal of Software, 2018, 29(10):3068-3090. (in Chinese)
[14] 刘怡然, 王东杰, 邓雪峰, 等. 基于长短时记忆神经网络的生猪价格预测模型[J]. 江苏大学学报(自然科学版), 2021, 42(2):190-197. Liu Y R, Wang D J, Deng X F, et al. Prediction method of hog price based on long short term memory network model[J]. Journal of Jiangsu University (Natural Science Edition), 2021, 42(2):190-197. (in Chinese)
[15] 杨大为, 冯辅周, 赵永东, 等. VMD样本熵特征提取方法及其在行星变速箱故障诊断中的应用[J]. 振动与冲击, 2018, 37(16):198-205. Yang D W, Feng F Z, Zhao Y D, et al. A VMD sample entropy feature extraction method and its application in planetary gearbox fault diagnosis[J]. Journal of Vibration and Shock, 2018, 37(16):198-205. (in Chinese)
