A hybrid ARIMA and neural network model applied to forecast catch volumes of Selar crumenophthalmus

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dc.contributor.authorAquino, Ronald L.
dc.contributor.authorAlcantara, Nialle Loui Mar T.
dc.contributor.authorAddawe, Rizavel C.
dc.date.accessioned2026-10-30T02:19:35Z
dc.date.available2026-10-30T02:19:35Z
dc.date.issued2017
dc.descriptionAbstract only
dc.description.abstractThe Selar crumenophthalmus with the English name big-eyed scad fish, locally known as matang-baka, is one of the fishes commonly caught along the waters of La Union, Philippines. The study deals with the forecasting of catch volumes of big-eyed scad fish for commercial consumption. The data used are quarterly caught volumes of big-eyed scad fish from 2002 to first quarter of 2017. This actual data is available from the open stat database published by the Philippine Statistics Authority (PSA)whose task is to collect, compiles, analyzes and publish information concerning different aspects of the Philippine setting. Autoregressive Integrated Moving Average (ARIMA) models, Artificial Neural Network (ANN) model and the Hybrid model consisting of ARIMA and ANN were developed to forecast catch volumes of big-eyed scad fish. Statistical errors such as Mean Absolute Errors (MAE) and Root Mean Square Errors (RMSE) were computed and compared to choose the most suitable model for forecasting the catch volume for the next few quarters. A comparison of the results of each model and corresponding statistical errors reveals that the hybrid model, ARIMA-ANN (2,1,2)(6:3:1), is the most suitable model to forecast the catch volumes of the big-eyed scad fish for the next few quarters.
dc.identifier.citationAquino, R. L., Alcantara, N. L. M. T., & Addawe, R. C. (2017). A hybrid ARIMA and neural network model applied to forecast catch volumes of selar crumenophthalmus. IMT-GT International Conference on Mathematics, Statistics and their Applications, 1905. https://doi.org/10.1063/1.5012225
dc.identifier.doihttps://doi.org/10.1063/1.5012225
dc.identifier.isbn978-073541595-9
dc.identifier.issn0094243X
dc.identifier.urihttps://lakasa.dmmmsu.edu.ph/handle/123456789/2865
dc.language.isoen
dc.publisherAmerican Institute of Physics Inc.
dc.sdgSDG 14
dc.sdgSDG 2
dc.subject.ddcFisheries
dc.subject.lcshFisheries--Catch effort--Mathematical models
dc.subject.lcshNeural networks (Computer science)
dc.titleA hybrid ARIMA and neural network model applied to forecast catch volumes of Selar crumenophthalmus
dc.typeConference Paper
local.subject.scientificnameSelar crumenophthalmus
oaire.citation.volume1905
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