Predictive Modeling of Pelagic Fish Catch using Seasonal ARIMA Models

Abstract

Fish catch prediction is an important problem in the fisheries sector and has a long history of research. The main goal of this paper is to create a model and make predictions using fish catch data of two fish species. Among the most effective and prominent approaches for analyzing time series data is the methods introduced by Box and Jenkins. In this study we applied the Box-Jenkins methodology to build Seasonal Autoregressive Integrated Moving Average (SARIMA) model for monthly catches of two fish species for a period of five years (2007 -2011). The seasonal ARIMA (1, 1, 0)(0, 0, 1) 12 and SARIMA (0, 1, 1) (0, 0, 1) 12 models were found fit and confirmed by the Ljung-Box test and these models were used to forecast 5 months upcoming catches of Trichiurus lepturus (Ikan Selayor) and Amblygaster leiogaster (Tambun Beluru) fish species. The result will help decision makers to establish priorities in terms of fisheries management.

A modified EMD-ARIMA based on clustering analysis for fishery landing forecasting

“…In recent literatures, many forecasting models have been applied to forecast the landings and catch per unit effort of many fish and invertebrate such as linear regression, moving average, autoregressive integrated moving average (ARIMA), Artificial Neural Networks (ANN), fuzzy methods, fuzzy expected interval models [1,2,5,6,8,10,13,14,17]. For modeling fisheries sciences time series data, ARIMA models based on the stochastic theory have been widely used for time series modeling.…”

Analisis Hubungan Produksi Perikanan Tangkap Laut, Curah Hujan, Dan Suhu Permukaan Laut Dengan Model Vector Autoregressive (Var)

“…Penelitian terkait metode VAR telah dilakukan oleh Ranangga, et al (2018) menunjukkan bahwa nilai MAPE pada model VAR (6) memberikan peramalan yang baik dalam meramalkan jumlah kunjungan wisatawan Cina (15,9%) dan memberikan hasil peramalan yang akurat dalam meramalkan jumlah kunjungan wisatawan Australia (6,8%) dan Jepang (9%). Sementara itu, penelitian peramalan produksi penangkapan ikan telah dilakukan oleh Bako et al (2013) dengan menggunakan model ARIMA. Dari penelitian Bako et al (2013), diperoleh model ARIMA dan model SARIMA sebagai model terbaik dalam meramalkan jumlah penangkapan ikan selayor dan ikan tambun beluru di perairan Malaysia.…”

“…Sementara itu, penelitian peramalan produksi penangkapan ikan telah dilakukan oleh Bako et al (2013) dengan menggunakan model ARIMA. Dari penelitian Bako et al (2013), diperoleh model ARIMA dan model SARIMA sebagai model terbaik dalam meramalkan jumlah penangkapan ikan selayor dan ikan tambun beluru di perairan Malaysia. Sedangkan penelitian pengaruh oseanografi terhadap perikanan tangkap laut telah dilakukan oleh Perdana dan Susilowati (2015) dengan menggunakan metode pendekatan kualitatif secara deskriptif, memberikan hasil bahwa gejala-gejala perubahan iklim yang dirasakan oleh masyarakat nelayan di Kota Semarang yaitu, curah hujan dengan intensitas tinggi, angin kencang, dan gelombang tinggi, berdampak pada adanya perubahan volume hasil tangkapan yang mengakibatkan penurunan pendapatan nelayan dan perubahan biaya melaut.…”

Forecasting Modelling of Cockles in Malaysia by Using Time Series Analysis

“…The cockle trade also produces a generation of abundant waste shell. There are quite considerable studies were carried out to study the time series in the world [5,6,7,8,9].…”