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 |
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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. |
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ARIMA, MLP, RBF, MAE, MAPE and Residual Analysis
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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
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
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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Download@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}
}
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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