基于HP-EMD数据分解与CNN-LSTM深度学习的蔬菜价格预测模型
Vegetable price prediction model based on HP-EMD data decomposition and CNN-LSTM deep learning
何志亚 1刘闯 2武官府 3刘云贵 3马建强2
作者信息
- 1. 红河哈尼族彝族自治州水利水电勘察设计研究院,蒙自 661199
- 2. 河海大学农业科学与工程学院,南京 211100
- 3. 红河哈尼族彝族自治州水利水电工程地质勘察咨询规划研究院,蒙自 661199
- 折叠
摘要
现有蔬菜价格预测模型多针对单一品种且稳定性与适用性不足,鉴于此提出一种基于HP滤波法(Hodrick-Prescott filter)与经验模态分解法(Empirical mode decomposition,EMD)分解数据,并耦合卷积神经网络(Convolutional neural network,CNN)与长短期记忆模型(Long short-term memory,LSTM)的蔬菜价格预测模型.HP-EMD方法将价格序列分解为意义明确的分量以分析价格的波动规律,CNN-LSTM方法提取分量特征以提高模型的稳定性.以云南省2019-2021年西红柿、芹菜、菠菜、大白菜和大蒜的价格数据进行模型验证.结果表明:该模型预测的西红柿价格平均相对误差为5.03％、决定系数为0.85、均方根误差为0.30元(人民币,下同)/kg,DM检验(Diebold mariano test)表明该模型显著优于其他模型.其他蔬菜预测结果的决定系数也均在0.8以上,表明该模型具有良好的适用性.
Abstract
The existing vegetable price prediction models are mostly for single species and are not stable and applicable enough.We propose a method based on HP filtration(Hodric-Prescott filtration)and empirical mode decomposition(EMD)to decompose the data,and coupled with convolutional neural network(CNN)and long short-term memory.HP-EMD method decomposes the price series into meaningful components to analyze the price fluctuation pattern,and the CNN-LSTM method extracts the component features to improve the stability of the model.The model was validated with the price data of tomato,celery,spinach,cabbage and garlic in Yunnan Province from 2019-2021.The results showed that the model predicted tomato prices with an average relative error of 5.03％,coefficient of determination of 0.85,and root mean square error of 0.30 yuan/kg,and the DM test(Diebold mariano test)indicated that the model significantly outperformed the other models.The coefficients of determination of the other vegetable forecasts were also above 0.8,indicating that the model had good applicability.
关键词
蔬菜价格/CNN/LSTM/经验模态分解/HP滤波Key words
Vegetable prices/CNN/LSTM/Empirical modal decomposition/Hodric-Prescott filtration引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
维普
万方数据