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

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

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

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