Forecasting Wheat Production in India Using ARIMA and Radial Basis Function

Subbiah Selvakumar; Veluchamy Kasthuri

doi:doi:10.11648/j.ijdst.20220804.11

| Peer-Reviewed

Forecasting Wheat Production in India Using ARIMA and Radial Basis Function

Subbiah Selvakumar, Veluchamy Kasthuri

Published in International Journal on Data Science and Technology (Volume 8, Issue 4)

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

Views: Downloads:

Download PDF

Share This Article

Twitter
Linked In
Facebook

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

[1]	Arinjita Bhattacharyya., Tanujit Chakraborty and Shesh N. Rai (2022): Stochastic Forecasting of COVID – 19 Daily New Cases Across Countries With a Novel Hybrid Time Series Model, Springer, vol. 107, pp. 3025-3040.
[2]	Athira Va, Geetha Pb, Vinayakumar Rab, Soman K P (2018): DeepAirNet: Applying Recurrent Networks for Air Quality Prediction, International Conference on Computational Intelligence and Data Science, Procedia Computer Science, vol. 132 pp. 1394–1403.
[3]	Ben A. Smith (2020): A Novel IDEA: The Impact of Serial Interval on a Modified- Incidence Decay and Exponential Adjustment Model for Projections of Daily COVID-19 Cases, Infectious Disease Modelling, vol 5, pp. 346-356.
[4]	Hiransha M, Gopalakrishnan E. A, Vijay Krishna Menon, Soman K. P (2018): NSE Stock Market Prediction using Deep-Learning Models, International Conference on Computational Intelligence and Data Science, Procedia Computer Science, vol. 132, pp. 1351–1362.
[5]	Jayanth Balaji A., D. S. Harish Ram, Binoy B. Nair (2018): Applicability of Deep Learning Models for Stock Price Forecasting An Empirical Study on BANKEX Data, Procedia Computer Science, vol. 143, pp. 947–953.
[6]	Junling M (2020): Estimating Epidemic Exponential Growth Rate and Basic Reproduction Number, Infectious Disease Modelling, vol. 5, pp. 129-141.
[7]	Mao. S and F. Xiao (2019): Time Series Forecasting Based on Complex Network Analysis, IEEE, vol. 7, pp. 40220–40229, 2019.
[8]	Paarth Thadani (2021): Financial Forecasting using Stochastic Models: Reference from Multi-Commodity Exchange of India, Data Science in Finance and Economics, vol 1, pp. 196-214.
[9]	Rahim, N. F., Othman, M Sokkalingam, R and E. A. Kadir (2018): Forecasting Crude Palm Oil Prices Using Fuzzy Rule-Based Time Series Method, IEEE Access, vol. 6, pp. 32216 - 32224.
[10]	Rediat Takele (2020): Stochastic Modelling for Predicting COVID-19 Prevalence in East Africa Countries, Infectious Disease Modelling, vol. 5, pp 598-607.
[11]	Rout A. K., Dash P. K., Dash R., and Bisoi R. (2017): Forecasting Financial Time Series Using a Low Complexity Recurrent Neural Network and Evolutionary Learning Approach., Journal of King Saud University-Computer and Information Sciences, vol. 4, pp. 536-552.
[12]	Sathish, S, Khadar Babu SK. and C. U. Tripura Sundari (2019): Weather and Stochastic Forecasting Method for Generated Discharge Level Data at Sathanur Dam, International Journal of Innovative Technology and Exploring Engineering (IJITEE), vol. 8, pp. 2278-3075.
[13]	Sheetal S. Patil1, S. H. Patil, A. M. Pawar (2021): Study of Various Forecasting Models for Time Series Data Using Stochastic Processes, Journal of Computer Technology & Applications, vol. 12, pp. 26-32.
[14]	Thasleema S. C, and Ahammad Shaik (2019): Stochastic Time Series Modeling for Rice Production in Andhra Pradesh, International Journal of Scientific Research in Mathematics and Statistics, Vol. 6, pp. 50 -55.
[15]	Yousif Alyousifi, Mahmod Othman Andakram A and Almohammedi (2021): A Novel Stochastic Fuzzy Time Series Forecasting Model Based on a New Partition Method, IEEE, vol. 9, pp. 80236-80252.

Cite This Article

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download

TY - JOUR
T1 - Forecasting Wheat Production in India Using ARIMA and Radial Basis Function
AU - Subbiah Selvakumar
AU - Veluchamy Kasthuri
Y1 - 2022/11/29
PY - 2022
N1 - https://doi.org/10.11648/j.ijdst.20220804.11
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
SP - 61
EP - 66
PB - Science Publishing Group
SN - 2472-2235
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
IS - 4
ER -

Copy | Download

Author Information

Subbiah Selvakumar

Department of Statistics, Government Arts and Science College, Nagercoil, India
Veluchamy Kasthuri

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

Download PDF

Sections

Plain Text BibTeX RIS

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

Copy | Download

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

Copy | Download

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

Copy | Download

@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}
}

Copy | Download