Does future tuna landing stock meet the target? Forecasting tuna landing in Malaysia using seasonal ARIMA model

Aslina Nasir and Yeny Nadira Kamaruzzaman

International Journal of Social Economics, 2024, vol. 51, issue 11, 1434-1449

Abstract: Purpose - This study was conducted to forecast the monthly number of tuna landings between 2023 and 2030 and determine whether the estimated number meets the government’s target. Design/methodology/approach - The ARIMA and seasonal ARIMA (SARIMA) models were employed for time series forecasting of tuna landings from the Malaysian Department of Fisheries. The best ARIMA (p, d, q) and SARIMA(p, d, q) (P, D, Q)12model for forecasting were determined based on model identification, estimation and diagnostics. Findings - SARIMA(1, 0, 1) (1, 1, 0)12was found to be the best model for forecasting tuna landings in Malaysia. The result showed that the fluctuation of monthly tuna landings between 2023 and 2030, however, did not achieve the target. Research limitations/implications - This study provides preliminary ideas and insight into whether the government’s target for fish landing stocks can be met. Impactful results may guide the government in the future as it plans to improve the insufficient supply of tuna. Practical implications - The outcome of this study could raise awareness among the government and industry about how to improve efficient strategies. It is to ensure the future tuna landing meets the targets, including increasing private investment, improving human capital in catch and processing, and strengthening the system and technology development in the tuna industry. Originality/value - This paper is important to predict the trend of monthly tuna landing stock in the next eight years, from 2023 to 2030, and whether it can achieve the government’s target of 150,000 metric tonnes.

Keywords: Tuna landing; Stock; Forecast; ARIMA model; SARIMA model (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eme:ijsepp:ijse-03-2023-0233

DOI: 10.1108/IJSE-03-2023-0233

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