Research on Small-scale Agricultural Product Price Prediction Based on Decomposition and Integration Method
The key deployment of agricultural market work shows the determination of the country to adhere to the stable development of agricultural and rural markets,and also reflects the necessity of studying agricultural prod-uct price prediction.The fluctuation range of the price series of smallholder farmers'products is large,and the phenomenon of price sudden increase and decrease occurs frequently,which produces shocking news in society,which is not conducive to the stable development of the agricultural product market.Because the price series of small-scale agricultural products have obvious nonlinear and non-stationary characteristics,the prediction effect of a single model is not good.To this end,this paper proposes a combined prediction model based on"decompo-sition and integration".Firstly,the Whale Optimization Algorithm(WOA)is applied to the Variational Modal Decomposition(VMD)algorithm,and the Sample Entropy is used to solve the problem.SampEn is used as the fitness function to screen out the optimal parameters.Then,the optimized variational mode decomposition method is used to realize the multi-mode decomposition of the agricultural product price series,solve the modal confusion problem in the complex small-scale agricultural product series,and obtain the modal components that can reflect the different characteristics of the original series.Secondly,the decomposed components and residual sequences are integrated into the Long Short-Term Memory(LSTM)neural network,which is trained as the feature quantities of the original agricultural product price series,so as to enhance the learning ability of the LSTM neural network and improve the prediction accuracy of the combined model.In this study,the daily average price data of potato,lotus root,white radish,Chinese cabbage,broccoli and cabbage in Henan Province from January 1,2016 to December 31,2021 are selected as the research object,and the combined model method is used to predict the price series of six small-scale agricultural products.The root mean square error(RMSE)and coefficient of determination(R2)are used as the evaluation indicators of the prediction effect of the model.The experimental results show that the RMSE of the WOA-VMD-LSTM combination model is 0.292,0.381,0.129,0.125,0.782,0.142,respectively.The coefficient of determination is 0.755,0.971,0.947,0.907,0.911,and 0.973,respectively.The EMD-LSTM combination model and ARIMA model are used to predict six kinds of price series,and the prediction results of the three models are compared comprehensively.The RMSE values obtained by WOA-VMD-LSTM combination model for the price series of lotus root,white radish,Chinese cabbage,broccoli and cabbage are lower than those in the EMD-LSTM model and ARIMA model,and the determination coefficient values are higher than those of the other two prediction models.Although the coefficient of determination value obtained by the WOA-VMD-LSTM com-bined prediction model for potato sequences is not better than that in the ARIMA model,the RMSE value is 48.1%lower than that in the EMD-LSTM model and 47.2%lower than that in the ARIMA model.In summary,it can be concluded that the method of using whale optimization algorithm to optimize the variational mode decom-position model for sequence decomposition and using neural network to complete the sequence prediction of agricultural product price can effectively improve the price prediction accuracy.This study tries to explore the influence of meteorological temperature,economic policy,crop yield,planting area and other factors on the daily average price data of six agricultural products,but does not achieve good results.Therefore,this paper uses the method of sequence decomposition to achieve the purpose of extracting sequence features.In the subsequent research,the sequence components can be divided according to different frequencies,and the characteristics of high-frequency components can be deeply analyzed to achieve deep noise reduction.The combined prediction model proposed in this study can effectively improve the accuracy of small-scale agricultural product price prediction,which not only stabilizes the supply and demand relationship of agricultural product market,but also protects the interests of agricultural product suppliers and consumers,and has the value of popularization and application.
万方数据
维普
