An Algorithm for Predicting Coffee Prices Using ARIMA Model
DOI:
https://doi.org/10.47941/ijms.3103Keywords:
: Coffee Prices, ARIMA, Algorithm, Predicting, Arabica, RobustaAbstract
Purpose: In this study, an algorithm for predicting coffee prices was developed incorporating the ARIMA model. The Algorithm simplifies the process of finding the optimal parameter values (p,d,q) for the ARIMA model.
Methodology: Secondary data from the Uganda Coffee Development Authority monthly reports was used. The data involved monthly coffee prices of Arabica and Robusta coffee for the years 2014-2021. CRISP-DM methodology and Python programming language were used. Arabica and Robusta coffee prices for the years 2019-2022 were predicted.
Findings: The study showed seasonality in the prices.
Unique Contribution to Theory, Practice and Policy: The study recommends that coffee farmers, traders, cooperatives, and the Uganda Coffee Development Authority use the forecasting tool, link it to market information platforms for easy access to regular price updates, and enhance it to track seasonal price changes.
Downloads
References
N. et al. (2015) Verter, “Analysis of Coffee Production and Exports in,” no. February 2016, 2015.
C. Deina et al., “A methodology for coffee price forecasting based on extreme learning machines,” Inf. Process. Agric., 2021, doi: 10.1016/j.inpa.2021.07.003.
B. F. Byanyima, “Determinants of Export Volumes of Uganda’s Coffee, 1991-2007,” Makerere Univ. Institutional Repos., 2011.
L. Jassogne, P. Läderach, and P. Van Asten, “The Impact of Climate Change on Coffee in Uganda: lessons from a case study in the Rwenzori Mountains,” Oxfarm Res. Reports, 2013.
M. Bussolo, O. Godart, J. Lay, and R. Thiele, “The impact of coffee price changes on rural households in Uganda,” Agric. Econ., vol. 37, no. 2–3, pp. 293–303, 2007, doi: 10.1111/j.1574-0862.2007.00275.x.
K. Wedig and J. Wiegratz, “Neoliberalism and the revival of agricultural cooperatives: The case of the coffee sector in Uganda,” J. Agrar. Chang., 2018, doi: 10.1111/joac.12221.
Ugandan Coffee Supply Chain Risk Assessment. 2011.
B. Chiputwa, D. J. Spielman, and M. Qaim, “Food standards, certification, and poverty among coffee farmers in Uganda,” World Dev., 2015, doi: 10.1016/j.worlddev.2014.09.006.
J. Lynam, “The highland perennial farming system,” in Farming Systems and Food Security in Africa, 2019.
B. Algieri and A. Leccadito, “Extreme price moves: an INGARCH approach to model coexceedances in commodity markets,” Eur. Rev. Agric. Econ., 2021, doi: 10.1093/erae/jbaa030.
K. H. Wang, C. W. Su, R. Tao, and L. N. Hao, “Are there periodically collapsing bubble behaviours in the global coffee market?,” Agrekon, 2020, doi: 10.1080/03031853.2019.1631865.
A. Y. Gebisa, “Coffee Price Pridiction Using Machine-Learning Techniques: a Case of Ethiopian Commodity Exchange (Ecx),” no. July, 2019.
R. Kiboko and F. A. Q. Shs, “UCDA MONTHLY REPORT FOR JANUARY 2014,” no. January, pp. 1–9, 2014.
R. Kiboko and F. A. Q. Shs, “UCDA MONTHLY REPORT FOR FEBRUARY 2014,” no. February, pp. 1–10, 2014.
R. Kiboko and F. A. Q. Shs, “UCDA MONTHLY REPORT FOR MARCH 2014,” no. March, pp. 1–9, 2014.
R. Kiboko and F. A. Q. Shs, “UCDA MONTHLY REPORT FOR APRIL 2014,” no. April, pp. 1–11, 2014.
A. Shs, “UCDA MONTHLY REPORT FOR MAY 2014,” no. MAY, pp. 1–11, 2014.
J. J. Struthers, “Commodity Price Volatility: Causes, Policy Options and Prospects for African Economies,” in Logistics and Global Value Chains in Africa, 2019.
H. Sekanjako, “Coffee farmers told to embrace value addition,” New Vision, Oct. 01, 2020.
K. Sarirahayu and A. Aprianingsih, “Strategy to Improving Smallholder Coffee Farmers Productivity,” Asian J. Technol. Manag., 2018, doi: 10.12695/ajtm.2017.11.1.1.
M. Musumba and R. Sen Gupta, “Transmission of World Prices to Ugandan Coffee Growers in a Liberalised Economy,” Dev. Policy Rev., 2013, doi: 10.1111/dpr.12004.
K. T. Akoyi and M. Maertens, “Walk the Talk: Private Sustainability Standards in the Ugandan Coffee Sector,” J. Dev. Stud., 2018, doi: 10.1080/00220388.2017.1327663.
L. A. Richey and S. Ponte, “Brand Aid and coffee value chain development interventions: Is Starbucks working aid out of business?,” World Dev., 2021, doi: 10.1016/j.worlddev.2020.105193.
J. Tanuwijaya and S. Hansun, “LQ45 stock index prediction using k-nearest neighbors regression,” Int. J. Recent Technol. Eng., vol. 8, no. 3, pp. 2388–2391, 2019, doi: 10.35940/ijrte.C4663.098319.
D. M. Efendi and F. Ardhy, “Prediction Of Coffee Prices With Backpropagation Neural Networks,” Pros. Int. Conf. …, no. December 2003, pp. 153–163, 2019, [Online]. Available: https://jurnal.darmajaya.ac.id/index.php/icitb/article/view/2087.
P. J. Brockwell and R. A. Davis, Introduction to Time Series and Forecasting - Second Edition. 2002.
S. K. Dash and P. K. Dash, “Short-term mixed electricity demand and price forecasting using adaptive autoregressive moving average and functional link neural network,” J. Mod. Power Syst. Clean Energy, 2019, doi: 10.1007/s40565-018-0496-z.
R. R. Novanda et al., “A Comparison of Various Forecasting Techniques for Coffee Prices,” J. Phys. Conf. Ser., vol. 1114, no. 1, 2018, doi: 10.1088/1742-6596/1114/1/012119.
M. J. D. J. K. Mung’atu, “Forecasting the Unit Price of Coffee Export in Rwanda Using Arima Model,” Int. J. Sci. Res., vol. 7, no. 11, pp. 375–380, 2018, doi: 10.21275/ART20192600.
G. Bandyopadhyay, “Gold Price Forecasting Using ARIMA Model,” J. Adv. Manag. Sci., no. March, pp. 117–121, 2016, doi: 10.12720/joams.4.2.117-121.
V. Vijayakumar and N. Chilamkurti, “Editor-in-Chief for International Journal of Wireless Networks and Broadband Technologies,” Int. J. Internet Technol. Secur. Trans., vol. 10, no. 4, pp. 396–406, 2020, [Online]. Available: https://coinmarketcap.com.
E. Ostertagova and O. Ostertag, “Forecasting Using Simple Exponential Smoothing Method,” no. June, 2014, doi: 10.2478/v10198-012-0034-2.
E. Ostertagová and O. Ostertag, “Forecasting using simple exponential smoothing method,” Acta Electrotech. Inform., vol. 12, no. 3, 2013, doi: 10.2478/v10198-012-0034-2.
Tesyon Korjo Hwase and Abdul Joseph Fofanah, “Machine Learning Model Approaches for Price Prediction in Coffee Market using Linear Regression, XGB, and LSTM Techniques,” Int. J. Sci. Res. Sci. Technol., pp. 10–48, 2021, doi: 10.32628/ijsrst218583.
Z. Ismail, A. Yahya, and A. Shabri, “Forecasting gold prices using multiple linear regression method,” Am. J. Appl. Sci., vol. 6, no. 8, pp. 1509–1514, 2009, doi: 10.3844/ajassp.2009.1509.1514.
S. Mehtab and J. Sen, “A Robust Predictive Model for Stock Price Prediction Using Deep Learning and Natural Language Processing,” SSRN Electron. J., 2020, doi: 10.2139/ssrn.3502624.
C. Liu, Z. Hu, Y. Li, and S. Liu, “Forecasting copper prices by decision tree learning,” Resour. Policy, vol. 52, no. May, pp. 427–434, 2017, doi: 10.1016/j.resourpol.2017.05.007.
B. M. Henrique, V. A. Sobreiro, and H. Kimura, “Stock price prediction using support vector regression on daily and up to the minute prices,” J. Financ. Data Sci., 2018, doi: 10.1016/j.jfds.2018.04.003.
Y. Liu, Q. Duan, D. Wang, Z. Zhang, and C. Liu, “Prediction for hog prices based on similar sub-series search and support vector regression,” Comput. Electron. Agric., vol. 157, no. January 2018, pp. 581–588, 2019, doi: 10.1016/j.compag.2019.01.027.
Unknown, “What is the CRISP-DM methodology?,” SmartVision, 2019. .
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Celia Ahumuza, Pius Ariho

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.