Welcome to Francis Academic Press

Academic Journal of Computing & Information Science, 2021, 4(6); doi: 10.25236/AJCIS.2021.040615.

Garlic price forecast based on the combined model of time-frequency decomposition and neural network


Yuliang Feng

Corresponding Author:
Yuliang Feng

Department of Computer Science, Shandong Agricultural University, Tai'an, China


China's garlic production accounts for more than 70% of the world's total output, and it is one of the most profitable agricultural products exported by China. Since 2003, garlic prices have fluctuated sharply and frequently, which has brought severe challenges to the healthy and sustainable development of China's garlic industry. Based on the analysis and comparison of various price prediction models, this paper constructs an EEMD-GRU combined prediction model. The model first uses the EEMD decomposition method to decompose the garlic price series on multiple time scales, and obtains 12 IMF eigenmode functions, which are classified and integrated to obtain high-frequency, intermediate and low-frequency terms. The analysis found that the low-frequency and intermediate-frequency items have a high contribution rate to the garlic price, and their contribution rates add up to 94%. Import the reconstructed garlic price sequence into the GRU model, select 50 training times and 30 hidden neurons to predict the garlic price, and obtain the prediction results. The prediction results obtained by the EEMD-GRU combined model are combined with ARIMA and ARIMA through evaluation indicators such as MSE. Comparing the prediction results obtained by ARIMA-SVR and LSTM models, it is found that the EEMD-GRU combined model has the highest trend prediction accuracy, the smallest prediction error, and the prediction effect is significantly better than the ARIMA, ARIMA-SVR and LSTM models. This research can help garlic-related industries to make a reasonable market allocation.


garlic industry big data, price fluctuation characteristics, multiple time scales, price forecasting model, price forecasting system

Cite This Paper

Yuliang Feng. Garlic price forecast based on the combined model of time-frequency decomposition and neural network. Academic Journal of Computing & Information Science (2021), Vol. 4, Issue 6: 86-96. https://doi.org/10.25236/AJCIS.2021.040615.


[1] Liu Feng, Wang Rujing, Li Chuanxi. The application of ARIMA model in agricultural product price forecasting [J]. journal6, 2009, 45(025):238-239.

[2] Hu Yang, Zhang Chaoyang. Corn price forecast in Hebei Province based on ARIMA model [J]. Agriculture and Technology, 2020, 40(23):4.

[3] Ji Dawei. Construction of market price prediction model based on improved BP network [J]. Automation Technology and Application, 2020, v.39; No.300(06): 62-65.

[4] Zheng Wei, Wang Canqiang, Li Weide. Agricultural product price prediction model based on seasonal index adjustment and HGWO-SVR algorithm [J]. Statistics and Decision, 2018(19): 5.

[5] Cao Shuang, He Yucheng. SVM-ARIMA agricultural product price prediction model based on wavelet decomposition [J]. Statistics and Decision, 2015(13): 92-95.

[6] Yu Weige, Wu Huarui, Peng Cheng. Research on the combined model of vegetable short-term price prediction based on Lasso regression and BP neural network [J]. Smart Agriculture (Chinese and English), 2020, 2(03): 108-117.