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Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India

Author

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  • Ansari Saleh Ahmar

    (Department of Statistics, Universitas Negeri Makassar, Makassar 90223, Indonesia)

  • Pawan Kumar Singh

    (School of Humanities and Social Sciences, Thapar Institute of Engineering and Technology, Patiala 147004, India
    Department of Economics, Lakshmibai College, University of Delhi, Delhi 110052, India)

  • R. Ruliana

    (Department of Statistics, Universitas Negeri Makassar, Makassar 90223, Indonesia)

  • Alok Kumar Pandey

    (Centre for the Integrated and Rural Development, Banaras Hindu University, Varanasi 221005, India)

  • Stuti Gupta

    (RamManohar Lohia University, Faizabad 224001, India)

Abstract

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.

Suggested Citation

  • Ansari Saleh Ahmar & Pawan Kumar Singh & R. Ruliana & Alok Kumar Pandey & Stuti Gupta, 2023. "Comparison of ARIMA, SutteARIMA, and Holt-Winters, and NNAR Models to Predict Food Grain in India," Forecasting, MDPI, vol. 5(1), pages 1-15, January.
    • Handle: RePEc:gam:jforec:v:5:y:2023:i:1:p:6-152:d:1030637
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    References listed on IDEAS

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    1. Prabhu Pingali & Anaka Aiyar & Mathew Abraham & Andaleeb Rahman, 2019. "Transforming Food Systems for a Rising India," Palgrave Studies in Agricultural Economics and Food Policy, Palgrave Macmillan, number 978-3-030-14409-8, October.
    2. Kim, Sungil & Kim, Heeyoung, 2016. "A new metric of absolute percentage error for intermittent demand forecasts," International Journal of Forecasting, Elsevier, vol. 32(3), pages 669-679.
    3. Yuehjen E. Shao & Jun-Ting Dai, 2018. "Integrated Feature Selection of ARIMA with Computational Intelligence Approaches for Food Crop Price Prediction," Complexity, Hindawi, vol. 2018, pages 1-17, July.
    4. William W. Guo & Heru Xue, 2014. "Crop Yield Forecasting Using Artificial Neural Networks: A Comparison between Spatial and Temporal Models," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-7, January.
    5. Ferbar Tratar, Liljana & Strmčnik, Ervin, 2016. "The comparison of Holt–Winters method and Multiple regression method: A case study," Energy, Elsevier, vol. 109(C), pages 266-276.
    6. Mittal, Shivika & Dai, Hancheng & Fujimori, Shinichiro & Masui, Toshihiko, 2016. "Bridging greenhouse gas emissions and renewable energy deployment target: Comparative assessment of China and India," Applied Energy, Elsevier, vol. 166(C), pages 301-313.
    7. Lucie Michel & David Makowski, 2013. "Comparison of Statistical Models for Analyzing Wheat Yield Time Series," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-11, October.
    8. Pratap Singh Birthal & Pramod Kumar Joshi & Devesh Roy & Amit Thorat, 2013. "Diversification in Indian Agriculture toward High-Value Crops: The Role of Small Farmers," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 61(1), pages 61-91, March.
    9. Prabhu Pingali & Anaka Aiyar & Mathew Abraham & Andaleeb Rahman, 2019. "Indian Food Systems towards 2050: Challenges and Opportunities," Palgrave Studies in Agricultural Economics and Food Policy, in: Transforming Food Systems for a Rising India, chapter 0, pages 1-14, Palgrave Macmillan.
    10. Zhang, Guoqiang & Eddy Patuwo, B. & Y. Hu, Michael, 1998. "Forecasting with artificial neural networks:: The state of the art," International Journal of Forecasting, Elsevier, vol. 14(1), pages 35-62, March.
    11. Chakraborty, Tanujit & Chattopadhyay, Swarup & Ghosh, Indrajit, 2019. "Forecasting dengue epidemics using a hybrid methodology," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 527(C).
    12. Jikun Huang & Carl Pray & Scott Rozelle, 2002. "Enhancing the crops to feed the poor," Nature, Nature, vol. 418(6898), pages 678-684, August.
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