Forecasting World Tuna Catches with ARIMA-Spline and ARIMA-Neural Networks Models


  • Boonmee LEE Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani 94000, Thailand
  • Suhartono Department of Statistics, Faculty of Mathematics & Natural Sciences, Institut Teknologi Sepuluh Nopember, Surabaya, Indonesia
  • Apiradee LIM Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani 94000, Thailand
  • Sung Keuk AHN Department of Finance & Management Science, Carson College of Business, Washington State University, WA, USA



ARIMA-spline model, Ensemble forecasts, Neural networks, Nonlinear univariate forecasting, Tuna catches


Tuna is a renewable resource that has been managed regionally, but its worldwide capacity for regeneration is still little known. A time-series dataset of tuna catches was used to develop nonlinear univariate models for monitoring the sustainability of tuna catches. Two approaches were compared: 1) fitting an ARIMA-spline model to the volume of annual tuna catches and 2) combining neural networks with an ARIMA model to fit the annual changes in volume. These models offer competitive forecasting performance with small percentage errors. By averaging results of the best model developed in each of these approaches, our ensemble forecast predicts that world tuna catches will reach the optimal level of 5.09 million tons in 2025, remain stable thereafter until 2033, and start decreasing about 0.78 % annually. These models could be used by regional fishery management groups to discover discrepancies between such projections and other science-based estimations of the maximum sustainable output.


  • AnARIMA-spline model is practical for forecasting time series with uncertainties and complex interaction of variables
  • The plausibility of forecasts is essential as the goodness of fit for statistical model validation
  • The ensemble forecasts of results from modelling both catches and the changes of catches offer an alternative view for monitoring trend of fishery practices



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How to Cite

LEE, B. ., SUHARTONO, S. ., LIM, A. ., & AHN, S. K. . (2021). Forecasting World Tuna Catches with ARIMA-Spline and ARIMA-Neural Networks Models. Walailak Journal of Science and Technology (WJST), 18(17), Article 9726 (15 pages).