Forecasting Monthly Fish Landing in East Coast Peninsular Malaysia Using SARIMA and Artificial Neural Network

Abstract

Marine resources management has become vital nowadays due to the increases of awareness of these resources becoming limited. With regards to fish landing, it has been a crucial for the managers to make better decision making particularly the amount of fish stock need to be kept for future food security since fish is one of the main source of protein supply to the country. Hence, the objective of this study is to predict the Monthly Fish Landing in East Coast Peninsular Malaysia using Box–Jenkins Autoregressive Seasonal Autoregressive Integrated Moving Average (SARIMA) and Artificial Neural Networks (ANN). The data obtained were split into two sets with ratio of 80% and 20%, training data was set from January 2012 until December 2019 while testing data from January 2020 to December 2021. The SAS and MATLAB were used to analyse and obtaining the best model of prediction. Based on Box-Jenkin approach, the best model of SARIMA was observed in SARIMA (1,1,0)(0,1,1)12 with AIC and SBC reach the minimum value at AIC=20.67209 and SBC=20.77894. By taking lag variables of SARIMA model as the input for ANN, we then developed SARIMA-ANN model. The findings showed that the integration of SARIMA–ANN model proven to be a feasible option for more accurate prediction of monthly fish landing performance in East Coast Peninsular Malaysia.