Time series analysis and forecasting techniques applied on loliginid and ommastrephid landings in Greek waters

Abstract

Time series analysis techniques (ARIMA models), artificial neural networks (ANNs) and Bayesian dynamic models were used to forecast annual loliginid and ommastrephid landings recorded from the most important fishing ports in the Northern Aegean Sea (1984Sea ( -1999. The techniques were evaluated based on their efficiency to forecast and their ability to utilise auxiliary environmental information. Applying a "stepwise modelling" technique, namely by adding stepwise predictors and comparing the quality of fit, certain inferences concerning the importance of the predictors were made.The ARIMA models predicted the test data very precisely (high R 2 ), especially if the target time series contained a strong autoregressive character, after they were first differenced to obtain stationarity (R 2 > 0.96). The disadvantage of the ARIMA, as with most statistical models, is their assumption that the relationships and system parameters remain the same across the observation and forecasting periods.The influence of temperature on catches was mainly investigated by applying neural models, which predicted the monthly landings with high precision (R 2 = 0.89), even when incorporating in the model exclusively monthly SST descriptors. Similarly, ANN models of annual landings containing monthly mean temperatures provided high precision (R 2 = 0.87) and valuable inference concerning the possible effect of the SST in certain months.Bayesian dynamic models also provided a high precision (R 2 = 0.96). They combined the information of both environmental and landing time series, namely the monthly mean temperatures and the monthly seasonality of the landings. The impact factors estimated from the model have the form of time series representing the temperature effect.The results reveal that both the monthly and the annual landings can be predicted and that the Bayesian model is the best performer overall, characterised by a higher number of stable forecasts, and forecasts with higher precision and accuracy, than the other methods. It is evident, from application of the "stepwise modelling" technique, that the incorporation of temperature descriptors can significantly improve the model performance.

Shrimp and Fish Catch Landing Trends in Songkhla Lagoon, Thailand During 2003-2016

“…Significant regression, univariate and multivariate time series models were used to forecast monthly and annual marine fisheries' catches (Stergiou and Christow, 1996; Stergiou et al, 1997) of Loliginid and Ommastrephid (Georgakarakos et al, 2002(Georgakarakos et al, , 2006.…”

“…So far, this method has been successful in describing and forecasting fishery dynamics of broadly different species-significantly, demersal and pelagic species (Stergiou, 1990; Stergiou and Christow, 1996; Stergiou et al, 1997; Tsitsika et al, 2007), squid (Pierce and Boyle, 2003), mackerel (Lloret et al, 2000;Punzón et al, 2004), loliginid and ommastrephid (Georgakarakos et al, 2002(Georgakarakos et al, , 2006. Songkhla Lagoon is one of the two lagoons in the world that has Irrawaddy dolphins, an endangered species.…”

Yellowfin Tuna (Thunnusalbacares) Fishing Ground Forecasting Model Based On Bayes Classifier In The South China Sea

“…Statistical methods are widely used in tuna fishing ground forecasting in other sea areas that need sufficient historical data to analyze a relationship between the oceanic environment and the fish catch and then to forecast the future condition of the fishing ground with the relationship, such as linear regression model (LRM) [6], time series analysis [7], spatial overlay analysis [8], geostatisticalanalysis [9], Bayes probability model [10,11,12,13], etc. Among them, the Bayes probability model has a solid theoretical foundation of Mathematics.…”

Fishery Landing Forecasting Using Wavelet-Based Autoregressive Integrated Moving Average Models

“…For modelling fisheries sciences time series data, ARIMA model has been popular and widely chosen [1,2,[6][7][8][9]. The ARIMA model is the standard parametric forecasting model for statistical time series analysis since the 1970s.…”