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Forecasting Foodgrains Production Using Arima Model and Neural Network

Received: 17 January 2021     Accepted: 30 January 2021     Published: 31 August 2021
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Abstract

The time series is a set of values arranged in a specific order of time. Prediction and analysis of food grain is a vital role in agriculture statistics. The Agriculture Statistics System is very complete and provides data on a wide range of topics such as crop area and production, land use, irrigation, land holdings, agricultural prices and market intelligence, livestock, fisheries, forestry etc. Agricultural credit and subsidies also consider important supporting factors for agricultural growth. India is the world's largest producer of millets and second-largest producer of wheat, rice, and pulses. The present research work focused on production of food grains in India using time series data ranging from 1990- 91 to 2018-19. In this paper, Autoregressive Integrated Moving Average Model (ARIMA), Multilayer Perceptron (MLP) and Radial Basis Function (RBF) for predicting foodgrains of India were compared. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were compared. The results were displayed numerically and graphically.

Published in American Journal of Neural Networks and Applications (Volume 7, Issue 2)
DOI 10.11648/j.ajnna.20210702.12
Page(s) 30-37
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

ARIMA, MLP, RBF, MAE, MAPE and Residual Analysis

References
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[7] Moulana Mohammed, Roshitha Kolapalli, Niharika Golla, Siva Sai Maturi (2020), Prediction of Rainfall Using Machine Learning Techniques, International Journal of Scientific & Technology Research, vol 9, pp. 3236-3240.
[8] Osman Hegazy, Omar S. Soliman and Mustafa Abdul Salam A Machine Learning Model for Stock Market Prediction International Journal of Computer Science and Telecommunications [Volume 4, Issue 12, December 2013] ISSN 2047-3338.
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  • APA Style

    Veluchamy Kasthuri, Subbiah Selvakumar. (2021). Forecasting Foodgrains Production Using Arima Model and Neural Network. American Journal of Neural Networks and Applications, 7(2), 30-37. https://doi.org/10.11648/j.ajnna.20210702.12

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    ACS Style

    Veluchamy Kasthuri; Subbiah Selvakumar. Forecasting Foodgrains Production Using Arima Model and Neural Network. Am. J. Neural Netw. Appl. 2021, 7(2), 30-37. doi: 10.11648/j.ajnna.20210702.12

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    AMA Style

    Veluchamy Kasthuri, Subbiah Selvakumar. Forecasting Foodgrains Production Using Arima Model and Neural Network. Am J Neural Netw Appl. 2021;7(2):30-37. doi: 10.11648/j.ajnna.20210702.12

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  • @article{10.11648/j.ajnna.20210702.12,
      author = {Veluchamy Kasthuri and Subbiah Selvakumar},
      title = {Forecasting Foodgrains Production Using Arima Model and Neural Network},
      journal = {American Journal of Neural Networks and Applications},
      volume = {7},
      number = {2},
      pages = {30-37},
      doi = {10.11648/j.ajnna.20210702.12},
      url = {https://doi.org/10.11648/j.ajnna.20210702.12},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20210702.12},
      abstract = {The time series is a set of values arranged in a specific order of time. Prediction and analysis of food grain is a vital role in agriculture statistics. The Agriculture Statistics System is very complete and provides data on a wide range of topics such as crop area and production, land use, irrigation, land holdings, agricultural prices and market intelligence, livestock, fisheries, forestry etc. Agricultural credit and subsidies also consider important supporting factors for agricultural growth. India is the world's largest producer of millets and second-largest producer of wheat, rice, and pulses. The present research work focused on production of food grains in India using time series data ranging from 1990- 91 to 2018-19. In this paper, Autoregressive Integrated Moving Average Model (ARIMA), Multilayer Perceptron (MLP) and Radial Basis Function (RBF) for predicting foodgrains of India were compared. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were compared. The results were displayed numerically and graphically.},
     year = {2021}
    }
    

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  • TY  - JOUR
    T1  - Forecasting Foodgrains Production Using Arima Model and Neural Network
    AU  - Veluchamy Kasthuri
    AU  - Subbiah Selvakumar
    Y1  - 2021/08/31
    PY  - 2021
    N1  - https://doi.org/10.11648/j.ajnna.20210702.12
    DO  - 10.11648/j.ajnna.20210702.12
    T2  - American Journal of Neural Networks and Applications
    JF  - American Journal of Neural Networks and Applications
    JO  - American Journal of Neural Networks and Applications
    SP  - 30
    EP  - 37
    PB  - Science Publishing Group
    SN  - 2469-7419
    UR  - https://doi.org/10.11648/j.ajnna.20210702.12
    AB  - The time series is a set of values arranged in a specific order of time. Prediction and analysis of food grain is a vital role in agriculture statistics. The Agriculture Statistics System is very complete and provides data on a wide range of topics such as crop area and production, land use, irrigation, land holdings, agricultural prices and market intelligence, livestock, fisheries, forestry etc. Agricultural credit and subsidies also consider important supporting factors for agricultural growth. India is the world's largest producer of millets and second-largest producer of wheat, rice, and pulses. The present research work focused on production of food grains in India using time series data ranging from 1990- 91 to 2018-19. In this paper, Autoregressive Integrated Moving Average Model (ARIMA), Multilayer Perceptron (MLP) and Radial Basis Function (RBF) for predicting foodgrains of India were compared. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were compared. The results were displayed numerically and graphically.
    VL  - 7
    IS  - 2
    ER  - 

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Author Information
  • Department of Economics, Erode Arts and Science College, Erode, India

  • Department of Statistics, Government Arts and Science College, Nagercoil, India

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