Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India
The agriculture sector plays an essential function within the Indian economic system. Foodgrains provide almost all the calories and proteins. This paper aims to compare ARIMA, SutteARIMA, Holt-Winters, and NNAR models to recommend an effective model to predict foodgrains production in India. The ex...
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MDPI AG
2023-01-01
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| Series: | Forecasting |
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| Online Access: | https://www.mdpi.com/2571-9394/5/1/6 |
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| author | Ansari Saleh Ahmar Pawan Kumar Singh R. Ruliana Alok Kumar Pandey Stuti Gupta |
| author_facet | Ansari Saleh Ahmar Pawan Kumar Singh R. Ruliana Alok Kumar Pandey Stuti Gupta |
| author_sort | Ansari Saleh Ahmar |
| collection | DOAJ |
| description | The agriculture sector plays an essential function within the Indian economic system. Foodgrains provide almost all the calories and proteins. This paper aims to compare ARIMA, SutteARIMA, Holt-Winters, and NNAR models to recommend an effective model to predict foodgrains production in India. The execution of the SutteARIMA predictive model used in this analysis was compared with the established ARIMA, Neural Network Auto-Regressive (NNAR), and Holt-Winters models, which have been widely applied for time series prediction. The findings of this study reveal that both the SutteARIMA model and the Holt-Winters model performed well with real-life problems and can effectively and profitably be engaged for food grain forecasting in India. The food grain forecasting approach with the SutteARIMA model indicated superior performance over the ARIMA, Holt-Winters, and NNAR models. Indeed, the actual and predicted values of the SutteARIMA and Holt-Winters forecasting models are quite close to predicting foodgrains production in India. This has been verified by MAPE and MSE values that are relatively low with the SutteARIMA model. Therefore, India’s SutteARIMA model was used to predict foodgrains production from 2021 to 2025. The forecasted amount of respective crops are as follows (in lakh tonnes) 1140.14 (wheat), 1232.27 (rice), 466.46 (coarse), 259.95 (pulses), and a total 3069.80 (foodgrains) by 2025. |
| first_indexed | 2024-03-11T06:31:58Z |
| format | Article |
| id | doaj.art-5f1d1907bff9452bac70679662b8f06a |
| institution | Directory Open Access Journal |
| issn | 2571-9394 |
| language | English |
| last_indexed | 2024-03-11T06:31:58Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Forecasting |
| spelling | doaj.art-5f1d1907bff9452bac70679662b8f06a2023-11-17T11:08:15ZengMDPI AGForecasting2571-93942023-01-015113815210.3390/forecast5010006Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in IndiaAnsari Saleh Ahmar0Pawan Kumar Singh1R. Ruliana2Alok Kumar Pandey3Stuti Gupta4Department of Statistics, Universitas Negeri Makassar, Makassar 90223, IndonesiaSchool of Humanities and Social Sciences, Thapar Institute of Engineering and Technology, Patiala 147004, IndiaDepartment of Statistics, Universitas Negeri Makassar, Makassar 90223, IndonesiaCentre for the Integrated and Rural Development, Banaras Hindu University, Varanasi 221005, IndiaRamManohar Lohia University, Faizabad 224001, IndiaThe agriculture sector plays an essential function within the Indian economic system. Foodgrains provide almost all the calories and proteins. This paper aims to compare ARIMA, SutteARIMA, Holt-Winters, and NNAR models to recommend an effective model to predict foodgrains production in India. The execution of the SutteARIMA predictive model used in this analysis was compared with the established ARIMA, Neural Network Auto-Regressive (NNAR), and Holt-Winters models, which have been widely applied for time series prediction. The findings of this study reveal that both the SutteARIMA model and the Holt-Winters model performed well with real-life problems and can effectively and profitably be engaged for food grain forecasting in India. The food grain forecasting approach with the SutteARIMA model indicated superior performance over the ARIMA, Holt-Winters, and NNAR models. Indeed, the actual and predicted values of the SutteARIMA and Holt-Winters forecasting models are quite close to predicting foodgrains production in India. This has been verified by MAPE and MSE values that are relatively low with the SutteARIMA model. Therefore, India’s SutteARIMA model was used to predict foodgrains production from 2021 to 2025. The forecasted amount of respective crops are as follows (in lakh tonnes) 1140.14 (wheat), 1232.27 (rice), 466.46 (coarse), 259.95 (pulses), and a total 3069.80 (foodgrains) by 2025.https://www.mdpi.com/2571-9394/5/1/6ARIMASutteARIMANNARHolt-Wintersfood grain |
| spellingShingle | Ansari Saleh Ahmar Pawan Kumar Singh R. Ruliana Alok Kumar Pandey Stuti Gupta Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India Forecasting ARIMA SutteARIMA NNAR Holt-Winters food grain |
| title | Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India |
| title_full | Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India |
| title_fullStr | Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India |
| title_full_unstemmed | Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India |
| title_short | Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India |
| title_sort | comparison of arima suttearima and holt winters and nnar models to predict food grain in india |
| topic | ARIMA SutteARIMA NNAR Holt-Winters food grain |
| url | https://www.mdpi.com/2571-9394/5/1/6 |
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