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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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National Academy of Agricultural Sciences (NAAS)
NAAS Score: *5.38 (2019)
[Effective from January 1, 2019]
For more details click here

ICV 2017: 100.00
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2017: 100.00
For more details click here

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PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
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Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2017: 100.00
NAAS RATING 2018: 5.38

Int.J.Curr.Microbiol.App.Sci.2018.7(12): 880-889
DOI: https://doi.org/10.20546/ijcmas.2018.712.110


Time Series Forecasting Using ARIMA and ANN Models for Production of Pearl Millet (BAJRA) Crop of Karnataka, India
N. Vijay1 and G.C. Mishra2
1 Central MugaEri Research and Training Institute, Jorhat, Assam, India
2Institute of Agricultural Sciences, Banaras Hindu University, Varanasi, 221005, Uttar Pradesh, India
*Corresponding author
Abstract:

Time series prediction is a vital problem in many applications in nature science, agriculture, engineering and economics. The objective of the study is to examine the flexibility of artificial neural network model (ANN) in time series forecasting by comparing with classical time series ARIMA model. The data consist of area and production of Pearl millet (bajra) crop area (‘000 ha) and production (‘000 MT) from 1955-56 to 2014-15 were collected from “Agricultural Statistics at a Glance 2014-15, Karnataka, India were used in the study to demonstrate the effectiveness of the model. The experiment shows that ANN model outperform the ARIMA Models based on root mean (RMSE), MAPE and MSE.


Keywords: ARMA, ANN, RMSE, Forecasting
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How to cite this article:

Vijay, N. and Mishra, G.C. 2018. Time Series Forecasting Using ARIMA and ANN Models for Production of Pearl Millet (BAJRA) Crop of Karnataka, India.Int.J.Curr.Microbiol.App.Sci. 7(12): 880-889. doi: https://doi.org/10.20546/ijcmas.2018.712.110