Application of SARIMA Model in Cucumber Price Forecast-Reference-Cited by-同舟云学术

Application of SARIMA Model in Cucumber Price Forecast

Published:2013-08 Issue: Volume:373-375 Page:1686-1690
ISSN:1662-7482
Container-title:Applied Mechanics and Materials
language:
Short-container-title:AMM

Author:

Luo Chang Shou¹,Zhou Li Ying²,Wei Qing Feng¹

Affiliation:

1. Beijing Academy of Agriculture and Forestry Sciences

2. China Agricultural University

Abstract

The price of vegetables is difficult to predict. In order to find an effective method, this paper fully considers the seasonal variations, and uses the seasonal auto regressive integrated moving average model （SARIMA） to forecast the cucumber price. The experimental results indicate that the SARIMA(1,0,1)(1,1,1)12 fits the cucumber market prices exactly in the previous months. Its average fitting error is 17%. The forecast data of twelve months in 2011 is in line with the actual trend. Its average error reaches 25%. The SARIMA model is feasible for short-term warning of vegetable price.

Publisher

Trans Tech Publications, Ltd.

Link

https://www.scientific.net/AMM.373-375.1686.pdf

Reference8 articles.

1. J.J. WANG,Y.F. CHEN. Analysis and forecast of China's vegetable market in 2010 Agricultural outlook. （2010）.

2. Y.H. WANG,J.B. LI,W.H.Y. Contract Selling Behavior of Chinese Traditional Medicinal Material Growers. Guizhou agricultural sciences(2012).

3. J.Y. BAO,Z.M. LI,J. YANG. Investigation and analysis of vegetable prices and the main related factors of Lianyungang ,. Shanghai Vegetables(2007).

4. C.S. LUO. Research on the Prediction Method of Vegetable Price based on Neural Network and Genetic Algorithm. Bulletin of Science and Technology. (2011).

5. S.F. SUN,C.S. LUO. Research on the Prediction of Vegetable Price Based on the RBF Neural Network Model. Chinese agricultural science bulletin. (2011).

Cited by 18 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Cost Forecasting for Building Rebar under Uncertainty Conditions: Methodology and Practice;Buildings;2024-06-21

2. Forecasting cauliflower prices in Nepal: a comparative analysis using seasonal time series and nonlinear models;Cogent Food & Agriculture;2024-04-15

3. A Comparative Study of Gaussian Process Machine Learning and Time Series Analysis Techniques for Predicting Unemployment Rate;2024 16th International Conference on Computer and Automation Engineering (ICCAE);2024-03-14

4. Online SARIMA applied for short-term electricity load forecasting;Applied Intelligence;2023-12-28

5. MobCovid: Confirmed Cases Dynamics Driven Time Series Prediction of Crowd in Urban Hotspot;IEEE Transactions on Neural Networks and Learning Systems;2023