National Academy of Agricultural Sciences (NAAS)
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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 |
Purpose of present paper is to discuss STM methodology utilized for modelling time-series data in the present of trend, seasonal and cyclic fluctuations. Structural time series model are formulated in such a way that their components are stochastic, i.e. they are regard as being driven by random disturbances. Structural time series model are formulated in such a way that their components are stochastic, i.e. they are regard as being driven by random disturbances. The study mainly confined to secondary collected data from a period 2009-10 to 2014-15 data of promising varieties of chickpea yield. As these techniques, it may be mentioned that models are fitted to the data and coefficient parameter value obtained on the basis of the model are compared with the actual observation for assessing the accuracy of the fitted model. To validate the forecasting ability of the fitted models, for the three years with upper and lower limit. The maximum chickpea yield obtained consistently JG-11variety with forecast for the year 2017-18 obtained 15.36 q/ha and the minimum yield obtained with decreasing order are Vijay and JG-6.
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