Forecasting of Rice Cultivation in India–A Comparative Analysis with ARIMA and LSTM-NN Models

Paidipati, Kiran Kumar and Banik, Arjun (2020) Forecasting of Rice Cultivation in India–A Comparative Analysis with ARIMA and LSTM-NN Models. EAI Endorsed Transactions on Scalable Information Systems, 7 (24): e8. ISSN 2032-9407

[thumbnail of eai.13-7-2018.161409.pdf]
eai.13-7-2018.161409.pdf - Published Version
Available under License Creative Commons Attribution No Derivatives.

Download (2MB) | Preview


In India, due to the blessing by the outbreak of the National Food Security Mission, the production of cereals such as wheat, rice etc, has increased in an alarming rate. In this Study, forecasting is done with the help Auto Regressive Integrated Moving Average (ARIMA) and Long Short-Term Memory Neural Network (LSTM-NN) models on the basis of the historical data of rice cultivation from the year 1950-51 to 2017-18. The well fitted ARIMA models for the parameters such as Area under Cultivation (0,1,1), Production (0,1,1) and Yielding (2,2,1) are obtained from the significant spikes of their respective Auto Correlation Function (ACF) and Partial Auto Correlation Function (PACF) plots. But, the models fitted with a supervised deep learning neural network known as LSTM-NN are found much better time series forecasting model than the ARIMA models. The performances of these models validated with the Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) values. From the study, the LSTM-NN’s are more flexible and able to develop accurate models for predicting the behavior of agricultural parameters than the ARIMA models.

Item Type: Article
Uncontrolled Keywords: Food Security, Rice Cultivation, ARIMA and LSTM-NN Models
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 08 Oct 2020 13:53
Last Modified: 08 Oct 2020 13:53

Actions (login required)

View Item
View Item