Article Number: DRJAFS18631176


ISSN: 2354-4147

Vol.8 (9), Pp. 340-345, September 2020

Copyright © 2020

Author(s) retain the copyright of this article

Original Research Article

Research on Ginger Price Prediction Based on Prophet- Support Vector Machine (SVM) Combination Model

Teng Jinling

Wang Xiujuan

Zhang Yan

Wen Fujiang

Liu Pingzeng


The traditional time series forecasting algorithm is difficult to solve the nonlinear price forecasting problem, and the modern time series forecasting algorithm is very dependent on data. In this study, we propose a price forecasting model based on Prophet SVM and take ginger as an example. In the process of model construction, the price series is decomposed into trend term, periodic term, and random term by using the Prophet model, and then the prediction of trend and the periodic term is carried out by Prophet, and the random term is predicted by SVM. Finally, the forecast value of each component is combined to get the final prediction result. The experimental results show that the root mean square error (RMSE is 0.39) of the Prophet SVM combination model is less than that of Prophet (RMSE is 0.45) and that of SVM (RMSE is 1.47). The combined model has higher prediction accuracy and better prediction performance. Prophet SVM has higher accuracy in agricultural product price prediction, which provides an important scientific basis and theoretical support for agricultural product price prediction.

Keywords: Price, forecast, Prophet-SVM, combination model

 Received: July 30, 2020  Accepted: August 25, 2020  Published: September 30, 2020

Jinling Et Al

Copyright © 2020 Direct Research Journal of Agriculture and Food Science