Abstract Full Text:PDF Recommended articles Mail to Author How to Cite

¹ Islamic Azad University, Young Researchers and Elite Club, Shiraz Branch, Shiraz, Iran
² Iranian Fisheries Science Research Institute, Agricultural Research, Education and Extension Organization, Tehran, Iran DOI : 10.4194/1303-2712-v18_11_05 Viewed : 5276 - Downloaded : 2750 Forecasting the status of fish landings is a major tool for fisheries managers and policy makers in order to decide on sustainable management issues. In this paper, yearly landings kilka data from 1990 to 2014 were analyzed using time series model. Autoregressive (AR), Moving Average (MA) and Autoregressive Integrated Moving Average (ARIMA) were considered through the analysis to select appropriate model for forecasting. Based on Autocorrelation function (ACF), Partial Autocorrelation function (PACF) and degree of differentiation, ARIMA (0, 2, 3) model with the lowest normal Bayesian information criterion (BIC) and Akaike information criterion (AIC) value was selected. Results showed that Kilka catch will increase gradually in the coming years. However, the hypothesis that the commercial catches have reached their zero point could not be rejected. In conclusion, results of this study revealed despite government reduced fishing mortality in the recent years, potential risk of population collapse is still remained. Keywords : Forecasting, ARIMA, management, Kilka, Caspian Sea