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International Journal of Current Microbiology and Applied Sciences (IJCMAS)
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Index Copernicus ICI Journals Master List 2018 - IJCMAS--ICV 2018: 95.39 For more details click here
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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 2018: 95.39
Index Copernicus ICI Journals Master List 2017 - IJCMAS--ICV 2018: 95.39
For more details click here

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Original Research Articles

PRINT ISSN : 2319-7692
Online ISSN : 2319-7706
Issues : 12 per year
Publisher : Excellent Publishers
Email : editorijcmas@gmail.com / submit@ijcmas.com
Editor-in-chief: Dr.M.Prakash
Index Copernicus ICV 2018: 95.39
NAAS RATING 2020: 5.38

Int.J.Curr.Microbiol.App.Sci.2018.7(3): 1698-1707
DOI: https://doi.org/10.20546/ijcmas.2018.703.201


Statistical Modeling and Forecasting of Total Fish Production of India: A Time Series Perspective
Mahalingaraya1, Santosha Rathod1, Kanchan Sinha1, R.S. Shekhawat1 and Shashikala Chavan2
1ICAR-Indian Agricultural Statistics Research Institute, New Delhi-110012, India
2Department of Computer Science, Jamia Millia Islamia, New Delhi-110025, India
*Corresponding author
Abstract:

Indian fisheries and aquaculture is an important sector of food production, providing nutritional security to the food basket, contributing to the agricultural exports and engaging about fourteen million people in different activities. Fish production in India has increased at a higher rate compared to food grains, milk, egg and other food items. Constituting of about 6.3% of the global fish production, the Indian fisheries sector contributes to 1.1% of the GDP and 5.15% of the agricultural GDP. Forecasting is used to analyze the past and current behavior to forecasts the future fish production which intern provide an aid to decision-making and in planning for the future effectively and efficiently. Autoregressive integrated moving average (ARIMA) model is the most widely used model for forecasting time series. One of the main drawback of this model is the presumption of linearity. To model the series which contains nonlinear patterns, the artificial intelligence techniques like Artificial Neural Network (ANN) model commonly employed. In this paper an attempt has been made to forecast the raw jute productivity of India using ARIMA and ANN models. Empirical results clearly reveal that the machine learning techniques out performed the ARIMA model.


Keywords: ARIMA, India, Fish production, ANN, Time series
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How to cite this article:

Mahalingaraya, Santosha Rathod, Kanchan Sinha, R.S. Shekhawat and Shashikala Chavan. 2018. Statistical Modeling and Forecasting of Total Fish Production of India: A Time Series Perspective.Int.J.Curr.Microbiol.App.Sci. 7(3): 1698-1707. doi: https://doi.org/10.20546/ijcmas.2018.703.201