文章基本信息

标题：Regression Based Price Prediction of Staple Food Materials Using Multivariate Models
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作者：K. Venkateswara Rao ; D. Srilatha ; D. Jagan Mohan Reddy 等
期刊名称：Scientific Programming
印刷版ISSN：1058-9244
出版年度：2022
卷号：2022
DOI：10.1155/2022/4572064
语种：English
出版社：Hindawi Publishing Corporation
摘要：Profit margins for essential foodstuffs could be a demand rising problem. There are several variables influencing currency fluctuations. For example, the various variables of commodity food prices are climate, crude prices, and so on. Forecasting the fluctuating prices of basic foodstuffs is also relevant even for the government, producers, and customers. The article will use ARCH (autoregressive conditional heteroskedasticity) to forecast the essential food market considering external conditions. The findings agree well enough with the assessment price in the industry by employing two main approaches, ARCH and GARCH (generalized autoregressive conditional heteroskedasticity). For jalapeno, the best result (96.87%) in estimating the cost of employing ARCH is achieved. In the meantime, the best result (99.94%) for the basic food tomato is observed using GARCH. Proportionally, the ARCH is stronger than GARCH, since GARCH is very consistent without disrupting current information.