.svg){kind=logo}
FORECASTING THE SEAWEED PRODUCTION IN TAWI-TAWI USING SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE MODEL
Keywords:
SARIMA model, forecasting, moving averageDOI:
https://doi.org/10.17654/0972361724038Abstract
This study used the Box-Jenkins method to determine a mathematical model that best fits the data on the seaweed production (metric tons) of the six (6) municipalities in Tawi-Tawi from January 2010 to December 2020. This study used the time series analysis by employing the Box-Jenkins technique. After applying the standard procedure to identify and estimate the most appropriate and adequate model, the results show that the data is seasonal autoregressive. The SARIMA model showed that the seaweed production in Tawi-Tawi is seasonal and will slightly increase in succeeding years. The highest production usually comes in the month of June of each year. It is recommended that the results of this study will be used as the basis for the government of Tawi-Tawi to map out intervention plans for viable and sustainable seaweed production.
Received: November 15, 2023
Revised: April 9, 2024
Accepted: April 19, 2024
References
J. Black and A. White, eds., The bootstrap methodology in time series forecasting, Proceedings of CompStat2006, Springer-Verlag, 2006, pp. 1067-1073.
J. G. Brida and N. Garrido, Tourism forecasting using SARIMA models in Chilean regions, International Journal of Leisure and Tourism Marketing 2(2) (2009), 176-190.
G. Box and G. Jenkins, Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, 1970.
L. Chukwu, A. Ano and D. Asawalam, Effects of poultry manure and NPK fertilizer on soil properties and nutrient uptake of maize (Zea mays L.) plants growth in an ultisol, Proceedings of the 36th Annual Conference of the Soil Science Society of Nigeria (SSSN) on 7th-11th March, 2012, University of Nigeria, Nsukka, 2012.
C. Dritsaki, Forecast of SARIMA models: n application to unemployment rates of Greece, American Journal of Applied Mathematics and Statistics 4(5) (2016), 136-148. doi: 10.12691/ajams-4-5-1.
R. Fannoh, G. Orwa and G. J. K. Mungatu, Modeling the inflation rates in Liberia SARIMA approach, International Journal of Science and Research 3(6) (2012), 1360-1367.
S. Georgakarakos, J. Haralabous, V. Valavanis, C. Arvanitidis, D. Koutsoubas and A. Kapantagakis, Loliginid and ommastrephid stock prediction in Greek waters using time series analysis techniques, Bull. Mar. Sci. 71 (2002), 269-287.
S. W. Gikungu, A. G. Waititu and J. M. Kihoro, Forecasting inflation rate in Kenya using SARIMA model, American Journal of Theoretical and Applied Statistics 4(1) (2015), 15-18.
B. W. Green, Fertilizers in aquaculture, Feed and Feeding Practices in Aquaculture, Elsevier, 2015, pp. 27-52.
H. Kibunja, J. Kihoro and G. Orwa, Forecasting precipitation using SARIMA model: a case study of Mt. Kenya region, International Institute for Science, Technology and Education 4(11) (2014), 50-58.
V. Kotu and B. Deshpande, Time Series Forecasting, Data Science, 2nd ed., 2019.
T. Koutroumanidis, L. Iliadis and G. K. Sylaios, Time-series modeling of fishery landings using ARIMA models and fuzzy expected intervals software, Environ. Model. Softw. 21 (2006), 1711-1721.
M. R. J. Luhan, S. S. Avañcena and J. P. Mateo, Effect of short-term immersion of Kappaphycus alvarezii (Doty) Doty in high nitrogen on the growth, nitrogen assimilation, carrageenan quality, and occurrence of ice-ice disease, J. Appl. Phycol. 27(2) (2015), 917-922.
M. Z. Md Maarof, Z. I. Ismail and M. Fadzli, Optimization of SARIMA model using genetic algorithm method in forecasting Singapore tourist arrivals to Malaysia, Appl. Math. Sci. 8(170) (2014), 8481-8491.
O. A. Otu, A. George, O. Jude, M. H. Ifeyinwa and I. Andrew, Application of Sarima models in modelling and forecasting Nigeria’s inflation rates, American Journal of Applied Mathematics and Statistics 2(1) (2014), 16-28.
N. Prista, N. Diawara, M. J. Costa and C. Jones, Use of SARIMA models to assess data-poor fisheries: a case study with a sciaenid fishery off Portugal, Fishery Bulletin 109(2) (2011), 170-185.
O. A. Samuel, M. Bello and I. A. Auta, Seasonal autoregressive integrated moving average (SARIMA) model for the analysis of the frequency of monthly rainfall in Osun state, Nigeria, Physical Science International Journal 22(4) (2019), 1-9.
M. Sayeed, M. Alam, S. Sultana, M. Ali, M. Azad and M. Islam, Effect of inorganic fertilizer on the fish growth and production in polyculture system of Bangladesh, Uni. J. Zoo. Rajshahi Univ. 26 (2008). DOI:10.3329/ujzru.v26i0.705.
G. Saz, The efficacy of SARIMA models in forecasting inflation rates in developing countries: the case for Turkey, International Research Journal of Finance and Economics 62 (2011), 111-142.
A. Sharma. A review on the effect of organic and chemical fertilizers on plants, Int. J. Res. Appl. Sci. Eng. Tech. 5(2) (2017), 677-680.
A. B. Tahiluddin, S. N. Nuñal and S. M. S. Santander-de Leon, Inorganic nutrient enrichment of seaweed Kappaphycus: farmers’ practices and effects on growth and ice-ice disease occurrence, Regional Studies in Marine Science 55 (2022), 102593.
A. B. Tahiluddin, E. J. Diciano, R. J. F. Robles and J. P. Akrim, Influence of different concentrations of ammonium phosphate on nitrogen assimilation of red seaweed Kappaphycus striatus, J. Bio. Stud. 1(2) (2021), 39-44
D. Valderrama, J. Cai, N. Hishamunda and N. Ridler, eds., Social and economic dimensions of carrageenan seaweed farming, Fisheries and Aquaculture Technical Paper No. 580, Rome, FAO, 2013, 204 pp.
FAO, World Food and Agriculture - Statistical Yearbook 2020, Rome, 2020.
https://doi.org/10.4060/cb1329en.
Philippine Statistics Authority, A Year of Innovation, Annual Report, 2019.
Philippine Statistics Authority, Strengthening Digital Transformation and Improving Quality Statistical Products and Services: The Ways to Manage the New Normal, Annual Report, 2021.
STAT 510: Applied Time Series Analysis, Lecture Notes, Penn State, Eberly College of Science. https://online.stat.psu.edu/stat510/.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Pushpa Publishing House, Prayagraj, India
This work is licensed under a Creative Commons Attribution 4.0 International License.
____________________________
Attribution: Credit Pushpa Publishing House as the original publisher, including title and author(s) if applicable.
No Derivatives: Modifying or creating derivative works not allowed without written permission.
Contact Pushpa Publishing House for more info or permissions.
Journal Impact Factor: 
