International Peer-Reviewed JournalOpen AccessISSN 2456-8880
irejournals@gmail.com+91-7433024337

Home / Current Issue / Paper 1704101

1704101 Vol 6 · Issue 8 Download Paper

Forecasting Monthly Prices of Selected Fish Commodities in National Capital Region in the Philippines through ARIMA Modeling

Judy Ann M. Ibarra Cheldee Kimberly P. Sumo Patrice C. Valdra

Subject area: Management and Commerce  ·  Area of research: Business and Management

Abstract

The researchers aim to analyze the behavior of the prices of selected fish commodities in National Capital Region like bangus (milkfish), tilapia (cichlid fish), galunggong (blue mackerel scad), alumahan (long-jawed mackerel), tambakol (yellowfin tuna), dalagangbukid (mountain maiden), alimasag (crab), pusit (squid), hipon (shrimp) and lato (sea grapes) from data gathered for the years 2016-2022. A total of 84 observations and 10 variables are used in this research. The data were gathered from the Bureau of Fisheries and Aquatic Resources. This study used an application of EVIEWS to forecast prices of these selected fish commodities from 2023-2027 using the ARIMA model (Autoregressive Integrated Moving Average). The researchers followed a research paradigm to be applied to the data to come up with the expected output. The study showed the best ARIMA models for bangus ARIMA (1,1,1), tilapia ARIMA (1,1,1) galunggong ARIMA(1,1,1), alumahan ARIMA (1,1,1), tambakolARIMA(1,2,1), dalagangbukid ARIMA (8,1,8), alimasag ARIMA (6,1,6), pusit ARIMA (12,1,12), hipon ARIMA (1,1,1), and lato ARIMA (3,1,3). This study will be of importance in assessing future prices that affect the economy.

Keywords

ARIMA, EViews, Fishery, Forecasting, Prices

References

[1] American Research Thoughts, Urrutia, J. D., Mingo, F. L. T., & Balmaceda, C. N. M. (2015). Forecasting income tax revenue of the Philippines using autoregressive integrated moving average (Arima) modeling: A time series analysis. Figshare. https:// 9

[2] Archives: Price monitoring (metro manila) |. (n.d.). Gov.Ph. Retrieved February 11, 2023, from https://www.bfar.da.gov.ph/archives-price- monitoring-metro-manila/

[3] Azanza, R., Sanchez-Escalona, K., & Largo, D. (2022). A sustainable and inclusive blue economy for the Philippine Archipelago. 100 150 200 250 300 350 400 450 161718192021222324252627 ALIMASAGF 0 100 200 300 400 500 600 161718192021222324252627 PUSITF 200 250 300 350 400 450 500 550 161718192021222324252627 HIPONF 40 80 120 160 200 240 280 161718192021222324252627 LATOF Transactions of the National Academy of Science and Technology, 44(2022), 1–11. https://

[4] Bandyopadhyay, G. (2016). Gold price forecasting using ARIMA model. Journal of Advanced Management Science, 117–121. https://

[5] Camacho, A. S., & Macalincag-Lagua, N. (1988). The Philippine aquaculture industry. Seminar on Aquaculture Development in Southeast Asia, 8-12 September 1987, Iloilo City, Philippines, 91–116.

[6] Carpenter, K. E., & Springer, V. G. (2005). The center of the center of marine shore fish biodiversity: the Philippine Islands. Environmental Biology of Fishes, 72(4), 467– 480. https://

[7] Case study 4: Overview of freshwater aquaculture of Tilapia in the Philippines. (n.d.). Adb.org. Retrieved February 11, 2023, from https://www.adb.org/sites/default/files/evaluatio n-document/35936/files/aquaculture-phi.pdf

[8] Chen, T. P. (1976). Culture of milkfish (chanos chanos) as a means of increasing animal protein supply. Journal of the Fisheries Research Board of Canada, 33(4), 917 –919. https://

[9] Country fisheries trade: Philippines. (2020, April 2). SEAFDEC. http://www.seafdec.org/country- fisheries-trade-philippines/

[10] Fattah, J., Ezzine, L., Aman, Z., El Moussami, H., & Lachhab, A. (2018). Forecasting of demand using ARIMA model. International Journal of Engineering Business Management, 10, 184797901880867. https://

[11] Fisheries country profile: Philippines (2022). (2022, September 7). SEAFDEC. http://www.seafdec.org/fisheries-country- profile-philippines-2022/

[12] Fishery resources. (n.d.). Gov.Ph. Retrieved February 11, 2023, from https://psa.gov.ph/content/fishery-resources

[13] German, J. D., & Catabay, M. A. G. (2018). Analysis of milkfish supply chain in the Philippines: A case study in Dagupan, Pangasinan.

[14] Lachica-Aliño, L., Wolff, M., & David, L. T. (2006). Past and future fisheries modeling approaches in the Philippines. Reviews in Fish Biology and Fisheries, 16(2), 201–212. https://

[15] Licuanan, W. Y., Cabreira, R. W., & Aliño, P. M. (2019). The Philippines. In World Seas: an Environmental Evaluation (pp. 515–537). Elsevier.

[16] Lim, C. P., Matsuda, Y., & Shigemi, Y. (1995). Problems and constraints in Philippine municipal fisheries: The case of San Miguel Bay, Camarines Sur. Environmental Management, 19(6), 837 –852. https://

[17] Liu, J.-M., Borazon, E. Q., & Muñoz, K. E. (2021). Critical problems associated with climate change: a systematic review and meta-analysis of Philippine fisheries research. Environmental Science and Pollution Research International, 28(36), 49425 –49433. https://

[18] Maribeth P., A., Errah Layza I., A., Kemberly Jane R., A., Gerlyn Keene R., F., Jocel D., R., & Odinah L., C. (2016). Factors affecting the market price of fish in the northern part of Surigao Del Sur, Philippines. Journal of Environment and Ecology, 7(2), 34. https://

[19] Marie, J., 1&2, A., Ma, T. M., Sheree, A. A., Emilinda, M., Mendoza, T., Dapito, M. B., & Amparo, S. (n.d.). Assessment of fish and shellfish consumption of coastal barangays along the marilao-meycauayan-Obando river system (MMORS), Philippines. Org.My. Retrieved February 11, 2023, from https://nutriweb.org.my/mjn/publication/23- 2/j.pdf

[20] Palma, M. A. (Ed.). (2009). The Philippines as an Archipelagic and Maritime Nation: Interests, Challenges, and Perspectives. S. Rajaratnam School of International Studies. https://www.rsis.edu.sg/wp- content/uploads/rsis-pubs/WP182.pdf

[21] Philippine statistics authority. (n.d.). Gov.Ph. Retrieved February 11, 2023, from https://psa.gov.ph/fisheries-special- release/node/168778

[22] Tahi̇luddi̇n, A., & Terzi̇, E. (2021). An overview of fisheries and aquaculture in the Philippines. Journal of Anatolian Environmental and Animal Sciences. https://

[23] Urrutia, J. D., Abdul, A. M., & Atienza, J. B. E. (Eds.). (2019). Forecasting Philippines imports and exports using Bayesian artificial neural network and autoregressive integrated moving average. AIP Publishing. https://aip.scitation.org/ 85

[24] Urrutia, J. D., Diaz, J. L. B., & Mingo, F. L. T. (2017). Forecasting the quarterly production of rice and corn in the Philippines: A time series analysis. Journal of Physics. Conference Series, 820(1), 012007. https:// 6596/820/1/012007

[25] Urrutia, J. D., Mercado, J., Ebue, K. E. E., Raymundo, F. S., & Nobles, B. G. (2018). Analysis of factors influencing agricultural productivity in the Philippines. Indian Journal of Science and Technology, 11(20), 1–10. https:// 6

[26] Urrutia, J. D., Resurreccion, N. C., Visco, L. M. C., Bautista, L. A., Malvar, R. J., Oliquino, A. B., & Gano, L. A. (2018). Daily prediction of electricity rates of distribution utilities in Luzon. Indian Journal of Science and Technology, 11(20), 1 –8. https:// 9

[27] Urrutia, J. D., Villaverde, S. V., Algario, N. T., Malvar, R. J., Oliquino, A. B., & Gano, L. A. (2018). Forecasting the number of fire accidents in the Philippines through multiple linear regression. Indian Journal of Science and Technology, 11(20), 1 –7. https:// 1

How to cite this paper

Judy Ann M. Ibarra, Cheldee Kimberly P. Sumo, Patrice C. Valdra "Forecasting Monthly Prices of Selected Fish Commodities in National Capital Region in the Philippines through ARIMA Modeling" Iconic Research And Engineering Journals Volume 6 Issue 8 2023 Page 120-129
Judy Ann M. Ibarra, Cheldee Kimberly P. Sumo, Patrice C. Valdra "Forecasting Monthly Prices of Selected Fish Commodities in National Capital Region in the Philippines through ARIMA Modeling" Iconic Research And Engineering Journals, vol. 6, no. 8, Feb. 2023
Judy Ann M. Ibarra, Cheldee Kimberly P. Sumo, Patrice C. Valdra (2023). Forecasting Monthly Prices of Selected Fish Commodities in National Capital Region in the Philippines through ARIMA Modeling. Iconic Research And Engineering Journals, 6(8).
Judy Ann M. Ibarra, Cheldee Kimberly P. Sumo, Patrice C. Valdra "Forecasting Monthly Prices of Selected Fish Commodities in National Capital Region in the Philippines through ARIMA Modeling" Iconic Research And Engineering Journals, vol. 6, no. 8, Feb. 2023.
@article{1704101,
      author = {Judy Ann M. Ibarra, Cheldee Kimberly P. Sumo, Patrice C. Valdra},
      title = {Forecasting Monthly Prices of Selected Fish Commodities in National Capital Region in the Philippines through ARIMA Modeling},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {8},
      pages = {120-129},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1704101.pdf},
      abstract = {The researchers aim to analyze the behavior of the prices of selected fish commodities in National Capital Region like bangus (milkfish), tilapia (cichlid fish), galunggong (blue mackerel scad), alumahan (long-jawed mackerel), tambakol (yellowfin tuna), dalagangbukid (mountain maiden), alimasag (crab), pusit (squid), hipon (shrimp) and lato (sea grapes) from data gathered for the years 2016-2022. A total of 84 observations and 10 variables are used in this research. The data were gathered from the Bureau of Fisheries and Aquatic Resources. This study used an application of EVIEWS to forecast prices of these selected fish commodities from 2023-2027 using the ARIMA model (Autoregressive Integrated Moving Average). The researchers followed a research paradigm to be applied to the data to come up with the expected output. The study showed the best ARIMA models for bangus ARIMA (1,1,1), tilapia ARIMA (1,1,1) galunggong ARIMA(1,1,1), alumahan ARIMA (1,1,1), tambakolARIMA(1,2,1), dalagangbukid ARIMA (8,1,8), alimasag ARIMA (6,1,6), pusit ARIMA (12,1,12), hipon ARIMA (1,1,1), and lato ARIMA (3,1,3). This study will be of importance in assessing future prices that affect the economy.},
      keywords = {ARIMA, EViews, Fishery, Forecasting, Prices},
      month = {February},
  }