IDEAS home Printed from https://ideas.repec.org/a/gam/jforec/v5y2023i1p6-152d1030637.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-9394/5/1/6/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-9394/5/1/6/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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, December.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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).
    10. 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.
    11. 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.
    12. Jikun Huang & Carl Pray & Scott Rozelle, 2002. "Enhancing the crops to feed the poor," Nature, Nature, vol. 418(6898), pages 678-684, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Myladis R. Cogollo & Gilberto González-Parra & Abraham J. Arenas, 2021. "Modeling and Forecasting Cases of RSV Using Artificial Neural Networks," Mathematics, MDPI, vol. 9(22), pages 1-20, November.
    2. Makridakis, Spyros & Spiliotis, Evangelos & Assimakopoulos, Vassilios, 2020. "The M4 Competition: 100,000 time series and 61 forecasting methods," International Journal of Forecasting, Elsevier, vol. 36(1), pages 54-74.
    3. Petropoulos, Fotios & Apiletti, Daniele & Assimakopoulos, Vassilios & Babai, Mohamed Zied & Barrow, Devon K. & Ben Taieb, Souhaib & Bergmeir, Christoph & Bessa, Ricardo J. & Bijak, Jakub & Boylan, Joh, 2022. "Forecasting: theory and practice," International Journal of Forecasting, Elsevier, vol. 38(3), pages 705-871.
      • Fotios Petropoulos & Daniele Apiletti & Vassilios Assimakopoulos & Mohamed Zied Babai & Devon K. Barrow & Souhaib Ben Taieb & Christoph Bergmeir & Ricardo J. Bessa & Jakub Bijak & John E. Boylan & Jet, 2020. "Forecasting: theory and practice," Papers 2012.03854, arXiv.org, revised Jan 2022.
    4. Urfels, Anton & Mausch, Kai & Harris, Dave & McDonald, Andrew J. & Kishore, Avinash & Balwinder-Singh, & van Halsema, Gerardo & Struik, Paul C. & Craufurd, Peter & Foster, Timothy & Singh, Vartika & K, 2023. "Farm size limits agriculture's poverty reduction potential in Eastern India even with irrigation-led intensification," Agricultural Systems, Elsevier, vol. 207(C).
    5. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models: Evidence from European Financial Markets and Bitcoins," Forecasting, MDPI, vol. 5(2), pages 1-15, June.
    6. Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.
    7. Songqing Jin & Scott Rozelle & Julian Alston & Jikun Huang, 2005. "Economies Of Scale And Scope And The Economic Efficiency Of China'S Agricultural Research System," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 46(3), pages 1033-1057, August.
    8. Balkin, Sandy, 2001. "On Forecasting Exchange Rates Using Neural Networks: P.H. Franses and P.V. Homelen, 1998, Applied Financial Economics, 8, 589-596," International Journal of Forecasting, Elsevier, vol. 17(1), pages 139-140.
    9. Barrow, Devon & Kourentzes, Nikolaos, 2018. "The impact of special days in call arrivals forecasting: A neural network approach to modelling special days," European Journal of Operational Research, Elsevier, vol. 264(3), pages 967-977.
    10. Daniel Buncic, 2012. "Understanding forecast failure of ESTAR models of real exchange rates," Empirical Economics, Springer, vol. 43(1), pages 399-426, August.
    11. Apostolos Ampountolas & Titus Nyarko Nde & Paresh Date & Corina Constantinescu, 2021. "A Machine Learning Approach for Micro-Credit Scoring," Risks, MDPI, vol. 9(3), pages 1-20, March.
    12. Peng Zhu & Yuante Li & Yifan Hu & Qinyuan Liu & Dawei Cheng & Yuqi Liang, 2024. "LSR-IGRU: Stock Trend Prediction Based on Long Short-Term Relationships and Improved GRU," Papers 2409.08282, arXiv.org, revised Sep 2024.
    13. Fan, Jinyang & Liu, Wei & Jiang, Deyi & Chen, Junchao & Ngaha Tiedeu, William & Chen, Jie & JJK, Deaman, 2018. "Thermodynamic and applicability analysis of a hybrid CAES system using abandoned coal mine in China," Energy, Elsevier, vol. 157(C), pages 31-44.
    14. Ebrahimpour, Reza & Nikoo, Hossein & Masoudnia, Saeed & Yousefi, Mohammad Reza & Ghaemi, Mohammad Sajjad, 2011. "Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange," International Journal of Forecasting, Elsevier, vol. 27(3), pages 804-816, July.
    15. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    16. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    17. Hu, Xincheng & Banks, Jonathan & Wu, Linping & Liu, Wei Victor, 2020. "Numerical modeling of a coaxial borehole heat exchanger to exploit geothermal energy from abandoned petroleum wells in Hinton, Alberta," Renewable Energy, Elsevier, vol. 148(C), pages 1110-1123.
    18. Poonam Rani & Ajeet Kumar Sahoo, 2023. "Assessment of Productivity and Crop Diversification Pattern in Punjab Agriculture," Arthaniti: Journal of Economic Theory and Practice, , vol. 22(2), pages 251-270, December.
    19. Leung, Philip C.M. & Lee, Eric W.M., 2013. "Estimation of electrical power consumption in subway station design by intelligent approach," Applied Energy, Elsevier, vol. 101(C), pages 634-643.
    20. Sukhwinder Singh & Andrew D. Jones & Ruth S. DeFries & Meha Jain, 2020. "The association between crop and income diversity and farmer intra-household dietary diversity in India," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 12(2), pages 369-390, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jforec:v:5:y:2023:i:1:p:6-152:d:1030637. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.