Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model
Agribusiness employs more than 66 percent of India’s rural population and is the country’s economic backbone. Beat crop growth is essential for practical farming since it increases soil diversity and actual design, and it may be grown in blended frameworks. Crop growth rates, applicability, and yiel...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Journal of Food Quality |
| Online Access: | http://dx.doi.org/10.1155/2022/1139440 |
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| author | Surindar Gopalrao Wawale Malik Jawarneh P. Naveen Kumar Thomas Felix Jyoti Bhola Roop Raj Sathyapriya Eswaran Rajasekhar Boddu |
| author_facet | Surindar Gopalrao Wawale Malik Jawarneh P. Naveen Kumar Thomas Felix Jyoti Bhola Roop Raj Sathyapriya Eswaran Rajasekhar Boddu |
| author_sort | Surindar Gopalrao Wawale |
| collection | DOAJ |
| description | Agribusiness employs more than 66 percent of India’s rural population and is the country’s economic backbone. Beat crop growth is essential for practical farming since it increases soil diversity and actual design, and it may be grown in blended frameworks. Crop growth rates, applicability, and yields have not improved significantly over time in the United States. Crops are defined by their seasonality, derived nature of demand, and relatively inelastic pricing. The general purpose of this research is to demonstrate the usefulness of price forecasting for agricultural prices and validate it for rice, which is consumed more in Indian states, for the year 2022, using time series data from 2016 to 2021. Every year, data for 50 days is collected and multiplied. The range of ten and its multiple is used for predicting. The results were obtained through the use of univariate analysis. To develop grain price estimates, researchers used Autoregressive Integrated Moving Average (ARIMA) methods, and the precision of the forecasts was examined using conventional mean square error (MSE) and mean absolute percentage error (MAPE) standards. As proven by the outcomes of ARIMA price predictions, the ARIMA model’s efficacy as a tool for price forecasting was effectively demonstrated by realistic models of projected prices for 2020. Because the MSA and MAPE values were lower, the forecast was more accurate. In addition, the price forecasting in this model is dependent on government incentives. |
| format | Article |
| id | doaj-art-fd088eb688cb4b9ebdfdf157265590c6 |
| institution | OA Journals |
| issn | 1745-4557 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Food Quality |
| spelling | doaj-art-fd088eb688cb4b9ebdfdf157265590c62025-08-20T02:22:38ZengWileyJournal of Food Quality1745-45572022-01-01202210.1155/2022/1139440Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA ModelSurindar Gopalrao Wawale0Malik Jawarneh1P. Naveen Kumar2Thomas Felix3Jyoti Bhola4Roop Raj5Sathyapriya Eswaran6Rajasekhar Boddu7Agasti ArtsFaculty of Computing SciencesDepartment of Agricultural EconomicsDepartment of EconomicsElectronics & Communication Engineering DepartmentEducation DepartmentDepartment of Agricultural ExtensionDepartment of Software EngineeringAgribusiness employs more than 66 percent of India’s rural population and is the country’s economic backbone. Beat crop growth is essential for practical farming since it increases soil diversity and actual design, and it may be grown in blended frameworks. Crop growth rates, applicability, and yields have not improved significantly over time in the United States. Crops are defined by their seasonality, derived nature of demand, and relatively inelastic pricing. The general purpose of this research is to demonstrate the usefulness of price forecasting for agricultural prices and validate it for rice, which is consumed more in Indian states, for the year 2022, using time series data from 2016 to 2021. Every year, data for 50 days is collected and multiplied. The range of ten and its multiple is used for predicting. The results were obtained through the use of univariate analysis. To develop grain price estimates, researchers used Autoregressive Integrated Moving Average (ARIMA) methods, and the precision of the forecasts was examined using conventional mean square error (MSE) and mean absolute percentage error (MAPE) standards. As proven by the outcomes of ARIMA price predictions, the ARIMA model’s efficacy as a tool for price forecasting was effectively demonstrated by realistic models of projected prices for 2020. Because the MSA and MAPE values were lower, the forecast was more accurate. In addition, the price forecasting in this model is dependent on government incentives.http://dx.doi.org/10.1155/2022/1139440 |
| spellingShingle | Surindar Gopalrao Wawale Malik Jawarneh P. Naveen Kumar Thomas Felix Jyoti Bhola Roop Raj Sathyapriya Eswaran Rajasekhar Boddu Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model Journal of Food Quality |
| title | Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model |
| title_full | Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model |
| title_fullStr | Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model |
| title_full_unstemmed | Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model |
| title_short | Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model |
| title_sort | minimizing the error gap in smart framing by forecasting production and demand using arima model |
| url | http://dx.doi.org/10.1155/2022/1139440 |
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