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Forecasting Wheat Production in India Using ARIMA and Radial Basis Function

Received: 9 September 2022     Accepted: 14 October 2022     Published: 29 November 2022
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Abstract

The time series is an arrangement of values in a specific order of time. Time series analysis, mostly used for forecasting. Prediction and analysis of wheat is a vital role in agricultural statistics. Indian wheat is largely a soft/medium hard, medium protein, white bread wheat, somewhat similar to U.S. hard white wheat. India is the second largest producer of wheat. The Agriculture Statistics System is very complete and provides data on a wide range of topics such as crop area and production, land use, water irrigation, land holdings, etc. Agricultural credit and subsidies are also considered important supporting factors for agriculture growth. Food grain production covers the dominant part of the cropped area (65%) in Indian agriculture. India is the world's largest producer of millets and second-largest producer of wheat, rice, and pulses. The present research work focused on the production of wheat in India using time series data ranging from 2001 to 2021. In this paper, Autoregressive Integrated Moving Average Model (ARIMA) and Radial Basis Function (RBF) for predicting wheat production of India was compared. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were compared. The outcomes were displayed numerically and graphically.

Published in International Journal on Data Science and Technology (Volume 8, Issue 4)
DOI 10.11648/j.ijdst.20220804.11
Page(s) 61-66
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), 2022. Published by Science Publishing Group

Keywords

ARIMA, RBF, MAE, MAPE, RMSE, Residual Analysis

References
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Cite This Article
  • APA Style

    Subbiah Selvakumar, Veluchamy Kasthuri. (2022). Forecasting Wheat Production in India Using ARIMA and Radial Basis Function. International Journal on Data Science and Technology, 8(4), 61-66. https://doi.org/10.11648/j.ijdst.20220804.11

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    ACS Style

    Subbiah Selvakumar; Veluchamy Kasthuri. Forecasting Wheat Production in India Using ARIMA and Radial Basis Function. Int. J. Data Sci. Technol. 2022, 8(4), 61-66. doi: 10.11648/j.ijdst.20220804.11

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    AMA Style

    Subbiah Selvakumar, Veluchamy Kasthuri. Forecasting Wheat Production in India Using ARIMA and Radial Basis Function. Int J Data Sci Technol. 2022;8(4):61-66. doi: 10.11648/j.ijdst.20220804.11

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  • @article{10.11648/j.ijdst.20220804.11,
      author = {Subbiah Selvakumar and Veluchamy Kasthuri},
      title = {Forecasting Wheat Production in India Using ARIMA and Radial Basis Function},
      journal = {International Journal on Data Science and Technology},
      volume = {8},
      number = {4},
      pages = {61-66},
      doi = {10.11648/j.ijdst.20220804.11},
      url = {https://doi.org/10.11648/j.ijdst.20220804.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijdst.20220804.11},
      abstract = {The time series is an arrangement of values in a specific order of time. Time series analysis, mostly used for forecasting. Prediction and analysis of wheat is a vital role in agricultural statistics. Indian wheat is largely a soft/medium hard, medium protein, white bread wheat, somewhat similar to U.S. hard white wheat. India is the second largest producer of wheat. The Agriculture Statistics System is very complete and provides data on a wide range of topics such as crop area and production, land use, water irrigation, land holdings, etc. Agricultural credit and subsidies are also considered important supporting factors for agriculture growth. Food grain production covers the dominant part of the cropped area (65%) in Indian agriculture. India is the world's largest producer of millets and second-largest producer of wheat, rice, and pulses. The present research work focused on the production of wheat in India using time series data ranging from 2001 to 2021. In this paper, Autoregressive Integrated Moving Average Model (ARIMA) and Radial Basis Function (RBF) for predicting wheat production of India was compared. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were compared. The outcomes were displayed numerically and graphically.},
     year = {2022}
    }
    

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    AU  - Subbiah Selvakumar
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    Y1  - 2022/11/29
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    DO  - 10.11648/j.ijdst.20220804.11
    T2  - International Journal on Data Science and Technology
    JF  - International Journal on Data Science and Technology
    JO  - International Journal on Data Science and Technology
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.ijdst.20220804.11
    AB  - The time series is an arrangement of values in a specific order of time. Time series analysis, mostly used for forecasting. Prediction and analysis of wheat is a vital role in agricultural statistics. Indian wheat is largely a soft/medium hard, medium protein, white bread wheat, somewhat similar to U.S. hard white wheat. India is the second largest producer of wheat. The Agriculture Statistics System is very complete and provides data on a wide range of topics such as crop area and production, land use, water irrigation, land holdings, etc. Agricultural credit and subsidies are also considered important supporting factors for agriculture growth. Food grain production covers the dominant part of the cropped area (65%) in Indian agriculture. India is the world's largest producer of millets and second-largest producer of wheat, rice, and pulses. The present research work focused on the production of wheat in India using time series data ranging from 2001 to 2021. In this paper, Autoregressive Integrated Moving Average Model (ARIMA) and Radial Basis Function (RBF) for predicting wheat production of India was compared. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) were compared. The outcomes were displayed numerically and graphically.
    VL  - 8
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    ER  - 

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

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

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