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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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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 2017: 100.00
NAAS RATING 2018: 5.38

Int.J.Curr.Microbiol.App.Sci.2019.8(11): 840-850
DOI: https://doi.org/10.20546/ijcmas.2019.811.099


Time Series Forecasting of Price Volatility of Bivoltine Cocoons: An Application of GARCH Process and Artificial Neural Network
G.R. Halagundegowda*, B.M. Kantharaju and P. Kumaresan
Central Silk Board, Ministry of Textiles, Govt. of India, Karnataka, India
*Corresponding author
Abstract:

Accurate forecasting of prices of bivoltine mulberry cocoons is essential for planning and policy purposes. A study had been taken up to develop an appropriate model for forecasting the daily prices of biovoltine reeling mulberry cocoons by using the data collected for the period from 1st April 2010 to 31st of August 2019 from Government Cocoon Market (GCM), Ramanagaram, Karnataka. Generalized Auto Regressive Conditional Heteroscedastic (GARCH) and Artificial Neural Network (ANN) models were used to analyze the past behavior of time series data in order to make inferences about its future behavior for prices of bivoltine cocoons. A suitable model of GARCH process was identified based on the results of Akakie Information Criterion (AIC) and Bayesian Information Criterion (BIC).GARCH (3, 2) model exhibited lesser AIC and Schwarz criterion compared to other process. The estimates of GARCH (3, 2) model showed that the coefficients of mean and variance equation were statistically significant at both 1% and 5% level of significance. A multilayered perceptron neural network was fitted, which had an input layer with 15 input nodes, 1 hidden layer with 10 hidden nodes and an output layer with one output node. The forecasting performance was assessed for both the models by using Mean Square Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) and found that the fitted GARCH (3, 2) model was found to be better than ANN model.


Keywords: AIC, ANN, AR, BIC, Bivoltine Mulberry Cocoons, GARCH
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How to cite this article:

Halagundegowda, G.R., B.M. Kantharaju and Kumaresan, P. 2019. Time Series Forecasting of Price Volatility of Bivoltine Cocoons: An Application of GARCH Process and Artificial Neural Network.Int.J.Curr.Microbiol.App.Sci. 8(11): 840-850. doi: https://doi.org/10.20546/ijcmas.2019.811.099