Vegetable price prediction model based on HP-EMD data decomposition and CNN-LSTM deep learning
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.
国家科技期刊平台
NSTL
万方数据
维普
