Analysis of cyclical fish landings through ESTAR nonlinear time-series approach
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Authors
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Bishal Gurung
IASRI, New Delhi
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K. N. Singh
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. Prajneshu
IASRI, New Delhi
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Avnish Grover
Abstract
Exponential smooth transition autoregressive (ESTAR) is a family of parametric nonlinear time-series models capable of capturing the non-Gaussian characteristics of the time-series along with cyclical fluctuations. The present study is based on the time-series data on oilsardine landings from Kerala during the period 1961 to 2008. The parameters of the model were estimated by genetic algorithm (GA). From the analysis of data it was concluded that ESTAR model fitted through GA has performed better than ARIMA model.Downloads
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Submitted
2013-12-26
Published
2016-06-28
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License
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
The copyright of the articles published in Indian Journal of Fisheries vests with the Indian Council of Agricultural Research, who has the right to enter into any agreement with any organization in India or abroad engaged in reprography, photocopying, storage and dissemination of information contained in these journals. The Council has no objection in using the material, provided the information is being utilized for academic purpose but not for commercial use. Due credit line should be given to the ICAR where information will be utilized.How to Cite
Gurung, B., Singh, K. N., Prajneshu, ., & Grover, A. (2016). Analysis of cyclical fish landings through ESTAR nonlinear time-series approach. Indian Journal of Fisheries, 63(2). https://doi.org/10.21077/ijf.2016.63.2.36001-14
