Short-Term Forecasting of Pork Price Based on The ARIMA Model
DOI:
https://doi.org/10.54097/raf97w16Keywords:
ARIMATime Series Analysis; Pork Price Forecast; Macroeconomic Management.Abstract
China's large population requires a large and diverse amount of food, of which pork is one of the important agricultural products related to the national economy and people's livelihood. Based on the pork price data from 2010 to July 2023, this paper adopts the ARIMA time series model to forecast the pork price in the short term from August 2023 to February 2024 in order to provide a powerful reference for the macroeconomic management of the country. After testing, the ARIMA model has a certain guiding and reference value for predicting the high and low pork prices in the short term, which can provide a decision-making basis for the macroeconomic management of the country.
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ZHAO Shuang, MIAO Xingyu, BAI Lanxin. Analysis of spatial and temporal correlation and influencing factors of pork price[J]. China Price,2023,(06):102-104.
Nie Yunbin, Qiao Juan. Impact of the occurrence of African swine fever on the development of China's pig industry [J]. China Agricultural Science and Technology Herald,2019,21(01):11-17.
TANG Liqun,CHEN Xiaozhen. Research on the main influencing factors of pork price fluctuation in China under the background of the epidemic [J]. Price Monthly,2022, (04):1-7.
Liu Rongjie,Zhang Runze,Yu Jianbo. Investigation on the influencing factors of price fluctuation of agricultural products--taking pork price as an example [J]. Anhui Agricultural Bulletin,2021,27(18):124-127.
Hengzhong Mao. Study on price fluctuation and transmission mechanism of pork in China[D]. Chinese Academy of Agricultural Sciences,2020.
LIAO Park, HE Yeping, HE Tracey, LIU Xiangyu. Can pig price index insurance curb the "pig cycle"? [J]. Management Review,2022,34(03):31-40.
LIU Mingyue, LIU Fang, LIU Yazhao. An empirical study of China's pig price volatility based on the VAR model [J]. Chinese Journal of Animal Husbandry,2020,56(11):184-188.
Cai Tiantian. Research on supply chain forecasting method based on ARIMA (1,1,1) model [D]. Henan University of Science and Technology,2022
Gao Long. Research on small-lot material production demand forecasting model based on ARIMA [J]. Modern Information Technology,2023,7(15):97-101.
Yan Xiangxiang. Predicting Park green space area using ARIMA model [J]. Computer Science,2020,47(S2):531-534+556.
Zhang Yingying. Forecasting pork prices in China based on ARIMA model[J]. Commercial Exhibition Economy,2020, (05):21-23.03–1448.
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