上海农业学报2024，Vol.40Issue(2)：109-117,9.DOI:10.15955/j.issn1000-3924.2024.02.18
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
摘要
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分类
农业科技引用本文复制引用
何志亚,刘闯,武官府,刘云贵,马建强..基于HP-EMD数据分解与CNN-LSTM深度学习的蔬菜价格预测模型[J].上海农业学报,2024,40(2):109-117,9.基金项目
国家自然科学基金项目(51609082) （51609082）
