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:: دوره 15، شماره 3 - ( 1395 ) ::
جلد 15 شماره 3 صفحات 1107-1089 برگشت به فهرست نسخه ها
Prediction of global sea cucumber capture production based on the exponential smoothing and ARIMA models
چکیده:   (3770 مشاهده)

Sea cucumber catch has followed “boom-and-bust” patterns over the period of 60 years from 1950-2010, and sea cucumber fisheries have had important ecological, economic and societal roles. However, sea cucumber fisheries have not been explored systematically, especially in terms of catch change trends. Sea cucumbers are relatively sedentary species. An attempt was made to explore whether the time series analysis approach (exponential smoothing models and autoregressive integrated moving average (ARIMA) models) is also applicable to relatively sedentary species. This study was conducted to develop exponential smoothing and ARIMA models to predict the short-term change trends (2011-2020), according to the time series data for 1950-2010 collected from the FAO Fishstat Plus database. The study results show that the single exponential smoothing and ARIMA (1, 1, 1) models are best for predicting sea cucumber short-term catches, and the predictive powers of both models are good. However, the accuracies of the models would be better if the data quality was resolved and the variables influencing sea cucumber capture production were fully considered.

متن کامل [PDF 377 kb]   (2069 دریافت)    
نوع مطالعه: پژوهشي | موضوع مقاله: Biology & physiology
دریافت: 1395/5/12 | پذیرش: 1395/5/12 | انتشار: 1395/5/12
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Yugui Z, Hongbing L, Jiansong C. Prediction of global sea cucumber capture production based on the exponential smoothing and ARIMA models. IJFS. 2016; 15 (3) :1107-1089
URL: http://jifro.ir/article-1-2330-fa.html

Prediction of global sea cucumber capture production based on the exponential smoothing and ARIMA models. مجله علوم شیلاتی ایران. 1395; 15 (3) :1107-1089

URL: http://jifro.ir/article-1-2330-fa.html



دوره 15، شماره 3 - ( 1395 ) برگشت به فهرست نسخه ها
Iranian Journal of Fisheries Sciences Iranian Journal of Fisheries Sciences
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