Forecasting Production of Rice In India – Using
Arima And Deep Learning Methods

Dr. S. A. Jyothi Rani; N. Chandan Babu

doi:https://doi.org/10.14445/22315373/IJMTT-V66I4P509

International Journal of Mathematics Trends and Technology

Research Article | Open Access | Download PDF

Volume 66 | Issue 4 | Year 2020 | Article Id. IJMTT-V66I4P509 | DOI : https://doi.org/10.14445/22315373/IJMTT-V66I4P509

Forecasting Production of Rice In India – Using Arima And Deep Learning Methods

Dr. S. A. Jyothi Rani, N. Chandan Babu

Abstract

In this paper, forecasting of production of Rice (Million Tones) using Auto Regressive Integrated Moving Averages (ARIMA) method, Recurrent Neural Network, Multilayer Perceptron (MLP) and Convolution Neural Networks (CNN) are presented. The appropriate best model is evaluated by comparing mean square error (MSE), Root mean square error (RMSE), mean absolute percentage error (MAPE). The study of the results shows that CNN is performing better than the other models ARIMA, RNN and MLP.

Keywords

ARIMA, Rice, RNN, CNN, MLP

References

[1] Rahul Tripathi (2014): Research Article | Open Access, Volume 2014 |Article ID 621313 | 9 pages | https://doi.org/10.1155/2014/621313, Forecasting Rice Productivity and Production of Odisha, India, Using Autoregressive Integrated Moving Average Models. [2] N. N. Jambhulkar (2013): „Modeling and forecasting of rice production in West Bengal using ARIMA model‟ under theme VI „Socio- economics is ues and livelihood security‟ in an ARRW Golden Jubilee International Symposium on “Sustainable Rice Production and Livelihood Security: Challenges and Opportunities” held at C. R. R. I. Cuttack from 2-5th March 2013.
[3] E. Manjula, S. Djodiltachoumy (2017): International Journal of Computational Intelligence and Informatics, Vol. 6: No. 4, March 2017 ISSN: 2349-6363 298
[4] Dr. Yamin Hassan, Naranarayan Buragohain and Shahidul Islam (2018): International Journal of Development Research, Volume: 08, Article ID: 14166
[5] Narayanan Balakrishnan and Dr.Govindarajan Muthukumarasamy (2016): International Journal of Computer Science and Software Engineering (IJCSSE), Volume 5, Issue 7, July 2016 ISSN (Online): 2409-4285 www.IJCSSE.org Page: 148-153.

Citation :

Dr. S. A. Jyothi Rani, N. Chandan Babu, "Forecasting Production of Rice In India – Using Arima And Deep Learning Methods," International Journal of Mathematics Trends and Technology (IJMTT), vol. 66, no. 4, pp. 59-63, 2020. Crossref, https://doi.org/10.14445/22315373/IJMTT-V66I4P509