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All Journal International Journal of Multidisciplinary: Applied Business and Education Research
Karl Evan R. Pama
Notre Dame of Marbel University
Civil Engineering, Building, Construction & Architecture Computer Science & IT Economics, Econometrics & Finance Environmental Science Medicine & Pharmacology

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Time Series Analysis of Commercial Yellowfin Tuna (Thunnus albacares) Production in South Cotabato, Philippines, Using the SARIMA Model Karl Evan R. Pama
International Journal of Multidisciplinary: Applied Business and Education Research Vol. 7 No. 8 (2026): International Journal of Multidisciplinary: Applied Business and Education Rese
Publisher : Future Science / FSH-PH Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/ijmaber.07.08.18

Abstract

A stochastic time series analysis was done through the SARIMA model to identify the characteristics of production in terms of trend and seasonality, develop the optimal SARIMA model based on certain information criteria and diagnostics, evaluate the accuracy of in-sample and out-of-sample forecast, and create a forecast for the production. Quarterly production data of commercial yellowfin tuna (Thunnus albacares) in South Cotabato during the years 2002 to 2021 were subjected to Box-Jenkins modeling procedure such as logarithmic transformation of data, differencing, checking for stationarity, identifying the optimal model through information criteria, estimating model parameters, and performing model diagnostics. It was found that the commercial yellowfin tuna production was fluctuating significantly but showed cyclical trend and seasonal pattern; the production is usually lower in Q3 but higher in Q2 and Q4. As for the results obtained from diagnostic checking, it could be deduced that SARIMA(1,1,1)(1,1,1)[4] model was deemed as the best-fitting model since it provided the minimum AIC and AICc values. Based on this model, it could be predicted in the next few years from 2026 to 2030 that there would be no remarkable growth or decline in production, and only slight fluctuation will be expected because of the season. It is essential to highlight, however, that there is a limit to the use of the univariate approach in time series forecasting, as even though the model will give accurate results when the environment remains constant, it does not account for sudden changes in ecological and economic environments.
Stochastic Time Series Analysis for Palay Production in South Cotabato, Philippines, Using the Seasonal Autoregressive Integrated Moving Average (SARIMA) Model Karl Evan R. Pama
International Journal of Multidisciplinary: Applied Business and Education Research Vol. 7 No. 9 (2026): International Journal of Multidisciplinary: Applied Business and Education Rese
Publisher : Future Science / FSH-PH Publications

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11594/ijmaber.07.09.11

Abstract

This study used SARIMA time series modeling to describe, model, and forecast quarterly palay production in South Cotabato, Philippines using data from 1987 to 2025 (i.e., 1987–2019 as training period and 2020–2025 as validation period), and then generate forecasts from 2026 to 2030. Specifically, it aimed to explore dynamics of palay production, determine the best SARIMA model specification, assess model forecasting ability, and create forecasts of future production. Results show that palay production shows a rising trend over the long term, together with a very significant seasonal variation with seasonality s = 4. Palay production peaks in Q3 and Q4 and bottoms out in Q2 every year. From among 29 potential model specifications, SARIMA(0,1,1)(1,1,1)[4] proved to be the optimal specification according to various criteria. The analysis of residuals shows that the model is good at capturing not only the seasonal component but also the non-seasonal one with residuals resembling white noise. Accuracy assessment of forecasts showed that the model has acceptable and stable accuracy both in-sample and out-of-sample. Even though the model underestimates production when validated, the errors made by the model in training data and validation data do not vary significantly, implying generalization without overfitting. The predicted level of palay production between 2026 to 2030 will be steady and feature recurring seasonal variations, with no increase or decrease in production over the five-year period considered. The prediction intervals widen as the length of prediction horizon increases, reflecting rising uncertainty about forecasts.
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