Abstract
ARIMA is a common model for non-stationary time series modeling, the model is essentially the combination of ARMA model and differentiate operation. This study indicated that most of the non-stationary time series can be differentially stationary by the difference of the appropriate order, and can be fitted by the ARMA model. As a relatively mature statistical method, ARIMA has been widely used in the price prediction of energy, agricultural products and stocks due to its advantages such as solid theoretical foundation and good short-term forecasting effect. In this paper, ARIMA was used to take the monthly corn price data of 23 months from April 2019 to February 2021 as the sample, processed and analyzed the data, and made a forecast analysis of the corn price in March 2021 in China. By comparing the experimental results with the actual price, it can be seen that the model established has a good fitting degree and can predict the corn price in China more accurately.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
