Prediction of global sea cucumber capture production based on the exponential smoothing and ARIMA models

Sea cucumber catch has followed “boom-and-bust” patterns over the period of ۶۰ years from ۱۹۵۰-۲۰۱۰, 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 (۲۰۱۱-۲۰۲۰), according to the time series data for ۱۹۵۰-۲۰۱۰ collected from the FAO Fishstat Plus database. The study results show that the single exponential smoothing and ARIMA (۱, ۱, ۱) 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.

Keywords:

Sea cucumber , Capture production , Prediction , Time series analysis , Exponential smoothing , ARIMA