Journal articles on the topic 'Box-Jenkins forecasting Computer programs'
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Zaiyong Tang, Chrys de Almeida, and Paul A. Fishwick. "Time series forecasting using neural networks vs. Box- Jenkins methodology." SIMULATION 57, no. 5 (November 1991): 303–10. http://dx.doi.org/10.1177/003754979105700508.
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Didiharyono, Didiharyono, and Bakhtiar Bakhtiar. "Forecasting Model with Box-Jenkins Method to Predict the Number of Tourists Visiting in Toraja." JEMMA (Journal of Economic, Management and Accounting) 1, no. 1 (October 25, 2018): 62. http://dx.doi.org/10.35914/jemma.v1i1.75.
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This study aims to determine forecasting model with Box-Jenkins method and obtain results of data forecasting the number of tourists visiting in Toraja (Tanah Toraja and North Toraja regency) the future period. Research method used is applied research with quantitative data. Research procedures include identification of model, parameter estimation in model, verification and forecasting with using Minitab computer software. Based on the research obtained four models used in forecasting the number of tourists in Toraja the future period is ARIMA(1,1,1), ARIMA(2,1,1), ARIMA(1,2,1) and ARIMA(2,2,1). The correct criteria in selecting the best model is the model that has the smallest Mean Square (MS) value. In this case the time series model with the smallest MS value is ARIMA(2,2,1) that is 736062253. Thus, this model will used in forecasting is ARIMA(2,2,1) with equations . The forecasting results for January to December 2021 is 149985, 193099, 207559, 202903, 222426, 229294, 239108, 250921, 260701, 271895, 283037 and 294221.
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Hadwan, Mohammad, Basheer M. Al-Maqaleh, Fuad N. Al-Badani, Rehan Ullah Khan, and Mohammed A. Al-Hagery. "A Hybrid Neural Network and Box-Jenkins Models for Time Series Forecasting." Computers, Materials & Continua 70, no. 3 (2022): 4829–45. http://dx.doi.org/10.32604/cmc.2022.017824.
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Thapa, Rabin, Shivahari Devkota, Sandip Subedi, and Babak Jamshidi. "Forecasting Area, Production and Productivity of Vegetable Crops in Nepal using the Box-Jenkins ARIMA Model." Turkish Journal of Agriculture - Food Science and Technology 10, no. 2 (March 1, 2022): 174–81. http://dx.doi.org/10.24925/turjaf.v10i2.174-181.4618.
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Forecasting of vegetable area, production, and productivity of Nepal was made from the historical data of 1977/78 to 2019/20 by using the Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) models. The best fitted ARIMA models were chosen based on the minimum value of the selection criterion, Akaike information criteria (AIC) and Bayesian information criteria (BIC). ARIMA (0, 2, 1) model was found suitable for all areas and production, whereas ARIMA (0, 2, 0) model was best fitted for forecasting vegetable productivity. The model was cross-validated by comparing the point prediction with the actual test series data from 2015/16 to 2019/20. The performances of models were determined from the mean absolute percent error (MAPE) value. The MAPE was found to be 2.70%, 2.40%, and 3.80%, respectively for the prediction of area, production, and productivity. The forecast was made for the immediate five years (2020/21 to 2024/25), and it showed an increasing value for area and production while the forecasts of productivity had lower values. The vegetable production policy in Nepal should be planned following accurate forecasts to increase production in the upcoming years. Awareness among the vegetable growers should be raised in the following years with appropriate extension programs.
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Sholl, Patricia, and R. Kenneth Wolfe. "The Kalman filter as an adaptive forecasting procedure for use with Box-Jenkins arima models." Computers & Industrial Engineering 9, no. 3 (January 1985): 247–62. http://dx.doi.org/10.1016/0360-8352(85)90005-1.
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Monika, Putri, Budi Nurani Ruchjana, and Atje Setiawan Abdullah. "The implementation of the ARIMA-ARCH model using data mining for forecasting rainfall in Bandung city." International Journal of Data and Network Science 6, no. 4 (2022): 1309–18. http://dx.doi.org/10.5267/j.ijdns.2022.6.004.
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A time series is a stochastic process which is arranged by time simultaneously. In this article, a time series model is used in accordance with Box-Jenkins' procedure. The Box-Jenkins procedure consists in identifying the model, estimating the parameters and diagnostic checking. The time series model is differentiated according to the number of variables, i.e. univariate and multivariate. The univariate method for the time series model that is often used is the Autoregressive Integrated Moving Average (ARIMA) model and the multivariate time series model is the Vector Autoregressive Integrated Moving Average (VARIMA) model. In this research, we studied the ARIMA model which is studied with a non-constant error variance. In this case, the Autoregressive Conditional Heteroscedasticity (ARCH) model is applied to outgrow the non-constant error variance. Selection of the best model by examining the minimum AIC for each model. The ARIMA-ARCH model is implemented on rainfall data in Bandung city with Knowledge Discovery in Database (KDD) in Data Mining. The methodology in the KDD process, including pre-processing, data mining process, and post-processing. Based on the results of model fitting, the best model is the ARIMA (2,1,4)-ARCH (1) model. The result of forecasting rainfall in Bandung shows a MAPE value is 11%, which has a similar pattern with actual data for short time 2-4 days. From these results, we conclude that the ARIMA-ARCH model is a good model for forecasting the rainfall in Bandung city.
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Popescu, Th D. "Experiences with a computer aided procedure for time series analysis and forecasting using Box-Jenkins philosophy." Annual Review in Automatic Programming 12 (January 1985): 361–64. http://dx.doi.org/10.1016/0066-4138(85)90062-x.
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Buchori, Mohammad, and Tedjo Sukmono. "Peramalan Produksi Menggunakan Metode Autoregressive Integrated Moving Average (ARIMA) di PT. XYZ." PROZIMA (Productivity, Optimization and Manufacturing System Engineering) 2, no. 1 (June 25, 2019): 27. http://dx.doi.org/10.21070/prozima.v2i1.1290.
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In production planning and control the first step is to forecast to determine how much production, the company forecasting is still not optimal, because forecasting has an important role in a company. PT. XYZ is a food company that produces chicken meatballs and chicken dumplings. So from that this study uses the forecasting method Autoregressive Integreted Moving Average (ARIMA). ARIMA is often also called the Box-Jenkins time series method. ARIMA is very good for short-term forecasting, while for long-term forecasting the forecasting accuracy is not good. The purpose of this research is to get a good ARIMA model, used to forecast production in the company. So that the production becomes optimal and not excessive which can cause waste of raw materials, which will make production costs a lot. Data processing is done with the help of an Eviews computer program to determine a good ARIMA model, from processing data obtained by ARIMA (1.0,0). With the results obtained forecasting in the period 37 to period 48.
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Siqueira, Hugo, Mariana Macedo, Yara de Souza Tadano, Thiago Antonini Alves, Sergio L. Stevan, Domingos S. Oliveira, Manoel H. N. Marinho, et al. "Selection of Temporal Lags for Predicting Riverflow Series from Hydroelectric Plants Using Variable Selection Methods." Energies 13, no. 16 (August 16, 2020): 4236. http://dx.doi.org/10.3390/en13164236.
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The forecasting of monthly seasonal streamflow time series is an important issue for countries where hydroelectric plants contribute significantly to electric power generation. The main step in the planning of the electric sector’s operation is to predict such series to anticipate behaviors and issues. In general, several proposals of the literature focus just on the determination of the best forecasting models. However, the correct selection of input variables is an essential step for the forecasting accuracy, which in a univariate model is given by the lags of the time series to forecast. This task can be solved by variable selection methods since the performance of the predictors is directly related to this stage. In the present study, we investigate the performances of linear and non-linear filters, wrappers, and bio-inspired metaheuristics, totaling ten approaches. The addressed predictors are the extreme learning machine neural networks, representing the non-linear approaches, and the autoregressive linear models, from the Box and Jenkins methodology. The computational results regarding five series from hydroelectric plants indicate that the wrapper methodology is adequate for the non-linear method, and the linear approaches are better adjusted using filters.
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Nigam, Bhanuj, and Dr A. C. Shukla. "SALES FORECASTING USING BOX JENKINS METHOD BASED ARIMA MODEL CONSIDERING EFFECT OF COVID -19 PANDEMIC SITUATION." International Journal of Engineering Applied Sciences and Technology 6, no. 7 (November 1, 2021): 87–97. http://dx.doi.org/10.33564/ijeast.2021.v06i07.015.
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This paper presents Box-Jenkins method used to forecast the future demand in a two wheeler industry. An automated technique in machine learning with the help of python language has been developed and used to analyze time series data and ultimately fit the model for future demand projection. The time series data is collected for the Royal Enfield bikes’ monthly sale available at the official website of Eicher motors ltd. The resulting pattern found in time series data is used to forecast the future behavior, knowledge of which will help to maintain the appropriate inventory and to reduce the risk in terms of changing customers preferences, resource availability etc. Also the effect of covid-19 pandemic has been captured to visualize its impact. The results thus obtained will be useful to understand the pattern if it occurs again in future. This method provides superior results and can be widely used in various forecasting scenario.
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Namahoro, Jean Pierre, and Adrien Mugabushaka. "Forecasting Maternal Complications Based on the Impact of Gross National Income Using Various Models for Rwanda." Journal of Environmental and Public Health 2020 (August 19, 2020): 1–8. http://dx.doi.org/10.1155/2020/7692428.
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Introduction. Preferably maternal mortalities are predominant in low- and middle-income countries (LMICs). In some African countries, including Rwanda, programs related to health-care delivery to reduce significantly severe complications including mortalities are established. Unfortunately, historical and forecasted maternal mortality reduction and the influence of gross national income (GNI) were not accessed. This study is aimed to forecast the three years of maternal mortalities (MMs) based on the influence of gross national income (GNI) in Rwanda. Methods. The period involved is from January 2009 to April 2018. Data analyzed were obtained from the Central Hospital of the University of Kigali (CHUK) and mined data from the WHO database. Time series approach (Box-Jenkins and exponential smoothing) and linear regression models were applied. Besides, IBM-SPSS and Eviews were used in the analysis. Results. The results revealed that MMs were not statistically different in several years, and there was a significant correlation between MMs and GNI (-0.610, P value 0.012 < 0.05). A double exponential smoothing model (DESM) was fitted for the best forecast and ARIMA (0,1,0) and linear regression models for a quick forecast. Conclusion. There was a slight effect of GNI in maternal mortality reduction, which leads to the steady decrease of the forecasted maternal mortality up to May 2021. The Government of Rwanda should intensively strengthen the health-care system, save the children programs, and support pregnant women by using GNI for reducing MMs at an advanced level.
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Chi, Yeong Nain. "Time Series Modeling and Forecasting of Monthly Mean Sea Level (1978 – 2020): SARIMA and Multilayer Perceptron Neural Network." International Journal of Data Science 3, no. 1 (June 30, 2022): 45–61. http://dx.doi.org/10.18517/ijods.3.1.45-61.2022.
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The primary purpose of this study was to demonstrate the role of time series model in predicting process and to pursue analysis of time series data using long-term records of monthly mean sea level from January 1978 to October 2020 at Grand Isle, Louisiana. Following the Box–Jenkins methodology, the ARIMA(1,1,1)(2,0,0)[12] with drift model was selected to be the best fitting model for the time series, according to the lowest AIC value in this study. Empirically, the results revealed that the MLP neural network model performed better compared to the ARIMA(1,1,1)(2,0,0)[12] with drift model at its smaller MSE value. Hence, the MLP neural network model not only can provided information which are important in decision making process related to the future sea level change impacts, but also can be employed in forecasting the future performance for local mean sea level change outcomes. Understanding past sea level is important for the analysis of current and future sea level changes. In order to sustain these observations, research programs utilizing the resulting data should be able to improve significantly our understanding and narrow projections of future sea level rise and variability.
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Monika, Putri, Budi Nurani Ruchjana, and Atje Setiawan Abdullah. "GSTARI-X-ARCH Model with Data Mining Approach for Forecasting Climate in West Java." Computation 10, no. 12 (November 23, 2022): 204. http://dx.doi.org/10.3390/computation10120204.
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The spatiotemporal model consists of stationary and non-stationary data, respectively known as the Generalized Space–Time Autoregressive (GSTAR) model and the Generalized Space–Time Autoregressive Integrated (GSTARI) model. The application of this model in forecasting climate with rainfall variables is also influenced by exogenous variables such as humidity, and often the assumption of error is not constant. Therefore, this study aims to design a spatiotemporal model with the addition of exogenous variables and to overcome the non-constant error variance. The proposed model is named GSTARI-X-ARCH. The model is used to predict climate phenomena in West Java, obtained from National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) data. Climate data are big data, so we used knowledge discovery in databases (KDD) in this study. The pre-processing step is collecting and cleaning data. Then, the data mining process with the GSTARI-X-ARCH model follows the Box–Jenkins procedure: model identification, parameter estimation, and diagnostic checking. Finally, the post-processing step for visualization and interpretation of forecast results was conducted. This research is expected to contribute to developing the spatiotemporal model and forecast results as recommendations to the relevant agencies.
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Ranganai, Edmore, and Mphiliseni B. Nzuza. "A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data." Journal of Energy in Southern Africa 26, no. 1 (March 23, 2015): 125–37. http://dx.doi.org/10.17159/2413-3051/2015/v26i1a2215.
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Extra-terrestrially, there is no stochasticity in the solar irradiance, hence deterministic models are often used to model this data. At ground level, the Box-Jenkins Seasonal/Non-seasonal Autoregressive Integrated Moving Average (S/ARIMA) short memory stochastic models have been used to model such data with some degree of success. This success is attributable to its ability to capture the stochastic component of the irradiance series due to the effects of the ever-changing atmospheric conditions. However, irradiance data recorded at the earth’s surface is rarely entirely stochastic but a mixture of both deterministic and stochastic components. One plausible modelling procedure is to couple sinusoidal predictors at determined harmonic (Fourier) frequencies to capture the inherent periodicities (seasonalities) due to the diurnal cycle, with SARIMA models capturing the stochastic components. We construct such models which we term, harmonically coupled SARIMA (HCSARIMA) models and use them to empirically model the global horizontal irradiance (GHI) recorded at the earth’s surface. Comparison of the two classes of models shows that HCSARIMA models generally out-compete SARIMA models in the forecasting arena.
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Halder, Amit Kumar, Ana S. Moura, and Maria Natália D. S. Cordeiro. "Moving Average-Based Multitasking In Silico Classification Modeling: Where Do We Stand and What Is Next?" International Journal of Molecular Sciences 23, no. 9 (April 29, 2022): 4937. http://dx.doi.org/10.3390/ijms23094937.
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Conventional in silico modeling is often viewed as ‘one-target’ or ‘single-task’ computer-aided modeling since it mainly relies on forecasting an endpoint of interest from similar input data. Multitasking or multitarget in silico modeling, in contrast, embraces a set of computational techniques that efficiently integrate multiple types of input data for setting up unique in silico models able to predict the outcome(s) relating to various experimental and/or theoretical conditions. The latter, specifically, based upon the Box–Jenkins moving average approach, has been applied in the last decade to several research fields including drug and materials design, environmental sciences, and nanotechnology. The present review discusses the current status of multitasking computer-aided modeling efforts, meanwhile describing both the existing challenges and future opportunities of its underlying techniques. Some important applications are also discussed to exemplify the ability of multitasking modeling in deriving holistic and reliable in silico classification-based models as well as in designing new chemical entities, either through fragment-based design or virtual screening. Focus will also be given to some software recently developed to automate and accelerate such types of modeling. Overall, this review may serve as a guideline for researchers to grasp the scope of multitasking computer-aided modeling as a promising in silico tool.
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Khariv, A., and A. Lagun. "AUTOMATED MODELING AND FORECASTING SYSTEM OF ENTERPRISE ACTIVITIES." Bulletin of Lviv State University of Life Safety 23 (June 30, 2021): 20–26. http://dx.doi.org/10.32447/20784643.23.2021.03.
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Introduction. Mathematical methods and models are an effective tool for studying complex economic systems at different levels of enterprise management. Economic mathematical modelling is actively developing not only as a scien-tific field but also as a means of substantiating management decisions in business, in the analysis and forecasting of socio-economic processes and phenomena. In the arsenal of economic and mathematical modelling now are using- modern computing methods and computer technology. Libraries of economic and mathematical models are an integral part of the architecture of decision support systems in specific areas of the economy. The rapid development of computer technology stimulates the emergence and formation of new theoretical volumes and applied areas of modelling.Purpose. Like any large and complex field of knowledge, mathematical modelling is evolving in different direc-tions, acquiring new flexible research methods. Therefore, based on new hardware, technology and software platforms it is necessary to create new information systems using economic and mathematical models in particular for forecasting of enterprise activities.Results. The article analyses the methods of modelling and forecasting the enterprise, considers the principles of software design. Using systems analysis, the design problem was analytically divided into parts. Also were investigated the connections and relationships between these parts, in particular, were implemented the problem tree and the goal tree. Implemented business process modelling performs based on created structural-logical diagrams, namely the IDEF0 dia-gram, which helps to visually display data and information that affect software development, a server part, input data and users. Using the results of research, the authors developed an automated information system for modelling and forecasting the activities of the enterprise, which uses models of Holt, Brown, exponential smoothing and Box-Jenkins for modelling. Part of the developed system is a designed software product that implements the objectives of the research. The obtained program results allow creating a clear forecast of the future activities of the enterprise.Conclusions. Based on the built graphs of modelling and forecasting of the Cisco Systems company financial activity with using of the developed automated system, we established that the Brown model is the best for providing educational sampling and a forecast of activity. The development of the automated system in the future involves the expansion of functionality, improvement and increasing of quality, as well as the creation of powerful analytics for more detailed forecasting.
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Nikolenko, P. G., and A. M. Terekhov. "Analysis of The State of The Tourism Industry in Russia аnd The Direction of Its Development." Statistics and Economics 19, no. 4 (July 14, 2022): 57–70. http://dx.doi.org/10.21686/2500-3925-2022-4-57-70.
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The purpose of the study. The purpose of the work is to assess the state of the tourism industry using statistical methods, analysis of scientific literature and identification of the main trends and promising directions of its development. The article is devoted to the possibilities of statistical analysis in the conditions of limited statistical information on Russia in relation to the tourism industry.Materials and methods. The following scientific methods are used in the article: analysis of scientific literature, analysis of dynamics and structure, coefficient analysis, correlation and regression analysis, graphical analysis, forecasting of indexes of the tourism industry. The analysis of Russian and foreign literature allowed us to formulate conclusions about modern methods of analysis used to assess the problems of the tourism industry. The analysis of the indexes in dynamics made it possible to identify and describe the main trends in the development of tourist flows, the volume and cost of selling tourist packages. The calculated structure of collective accommodation facilities showed their percentage ratio during the study period and the change in their shares. The use of correlation analysis made it possible to establish the closeness and direction of statistical relationships between individual factors affecting the state of the industry and the volume of tourist trips. A multiple regression model based on indexes characterizing the dynamics of prices for tours sold, the state of the transport sector and the food sector methodology is created. Based on the Box–Jenkins methodology, predictive models were created and medium-term forecasts were calculated for variables characterizing the number of tourist firms and the number of sanatorium-resort organizations and recreation organizations. The initial data for the study were Rosstat data in annual and spatial dimensions for the period 2011–2020.Results. The article highlights the main directions of research in the field of tourism industry, presented in the works of domestic and foreign authors. The foreign approach to its assessment is carried out through evidence-based methods. The research of domestic authors is more focused on the implementation of program-targeted methods and the identification of problems of assessment and prospects for the development of the industry in the Russian Federation. The analysis of the dynamics of inbound and outbound tourist trips showed a tendency to decrease their total number, which is due to the low level of tourist attractiveness, shocks and restrictive measures caused by the SARSCoV-2 pandemic, the tense foreign policy situation. It is established that the most active external tourist flows of the Russian Federation are observed with neighboring countries. Correlation analysis showed the presence of a statistical relationship between the number of trips made with the state of the transport infrastructure and the cost of the tour packages sold. The trend of growth in the implementation of tour packages in Russian destinations and a reduction in their number in foreign destinations, which is due to an increase in the disparity between the average cost of tour packages depending on the destination of the holiday, is established. The carried out regression analysis procedure showed the relationship between the number of tourist packages sold to citizens of the Russian Federation with the variables “average cost of sold tourist packages” and “fleet of aircraft”, which indicates the need to develop transport infrastructure and optimize the cost of recreation. Forecast calculations on the number of travel agencies and sanatorium-resort organizations have shown their decline in the medium term, which indicates the need to take appropriate measures to activate tourism entrepreneurship.Conclusion. Based on the results of the statistical analysis, the analysis of scientific literature and the content of tourism development programs in the Russian Federation, the authors identified common problems and directions of development of the industry. The main problems of the industry are the following: weak tourist attractiveness, insufficient development of tourist and transport infrastructure, lack of accommodation facilities aimed at mass tourists, low quality of domestic accommodation services, and high cost of foreign tours. The priority directions of the development of the domestic tourism business are related to: the introduction of modern digital technologies, active advertising of domestic tourism, optimization of the costs of tourism industry enterprises, activation of state support in difficult economic conditions.
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Intarapak, Sukanya, Thidaporn Supapakorn, and Witchanee Vuthipongse. "Classical Forecasting of International Tourist Arrivals to Thailand." Journal of Statistical Theory and Applications, March 19, 2022. http://dx.doi.org/10.1007/s44199-022-00041-5.
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AbstractThe objectives of this work are to find the suitable forecasting model and forecasting period of the number of foreign tourists traveling to Thailand. The monthly data is gathered during January 2008 to December 2019 and is divided into two sets. The first set is the data from January 2008 to December 2018 for the modelling by the method of decomposition, Holt–Winter’s exponential smoothing method and the Box–Jenkins. The second is the monthly data in 2019 for comparing the performance of the forecasting models via the criteria of the lowest mean absolute percentage error (MAPE) and the root mean square error (RMSE). The results show that, in term of forecasting, the multiplicative decomposition is the most accurate technique for the short-term (3 months) forecasting period with the lowest MAPE and RMSE of 1.04% and 42,054.29 international tourists, respectively.
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& Jbara, Mustafa. "FORECASTING THE FOOD GAP AND PRODUCTION OF WHEAT CROP IN IRAQ FOR THE PERIOD (2016-2025)." IRAQI JOURNAL OF AGRICULTURAL SCIENCES 49, no. 4 (September 1, 2018). http://dx.doi.org/10.36103/ijas.v49i4.63.
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Wheat is an important economic crop, various development projects adopted by the government to improve the level of production of all crops Despite the efforts to increase the production of grain crops, especially the wheat, the total production is still insufficient to meet the growing consumption needs, which led to widening of the food gap in addition to the increase in population and the increasing demand for food.The aim of this research is to Forecasting food gap and production of wheat in Iraq, Box-Jenkins one of forecasting method used to forecast production and food gap of wheat. Statistical programs Minitab and SPSS used to analyses data. The best method for Forecasting wheat production for the period 2025-2016 is ARIMA (4,1,3) based on significance of its parameters, as well as for having the lowest value of MSE which reached and owning the lowest value for(AIC). About food gap, the ARIMA model (1.0.1) was the best model for the same period in terms of having the lowest value (MSE) and the lowest value of AIC. The research reached a set of conclusions, There is an increase in the production of wheat in Iraq during the coming years (2025-2016) while offset by a semi-constant in the food gap for the same period and this indicates that the self-sufficiency of wheat in the short run can’t occur. Food gap for wheat is continuous and semi-fixed, indicating the expectation that self-sufficiency can’t be achieved in the short term, and the difficulty of covering consumption through the local production of wheat and having to fill the deficit by importing semi-fixed quantities during the subsequent period.
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Liana Neil C. Estoque, Leanna Marie D. Dela Fuente, Romie C. Mabborang, and Marivic G. Molina. "FORECASTING URBAN POPULATION GROWTH IN THE PHILIPPINES USING AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODEL." EPRA International Journal of Multidisciplinary Research (IJMR), July 14, 2022, 132–53. http://dx.doi.org/10.36713/epra10819.
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The Philippines is one of the fastest urbanizing countries in the East Asia and Pacific region (Baker & Watanabe, 2017). Despite having its advantages, urbanization still has its challenges that require extensive urban management and development programs for it to be prevented and minimized. In this paper, the researchers forecasted the urban population growth of the Philippines using the Autoregressive Integrated Moving Average (ARIMA) Model. The historical data obtained from the World Bank Group was from 1960 to 2020. The R Programming Language was used as the medium for the entire forecasting process. Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) plots, Augmented Dickey-Fuller (ADF) test, Phillips-Perron (PP) test, and Kwiatkowski, Phillips, Schmidt, and Shin (KPSS) test were used for testing the stationarity of the time-series data. Moreover, Akaike Information Criteria (AIC), Corrected Akaike Information Criterion (AICc), and Schwarz Information Criteria (SIC) were used as criteria for selecting the best ARIMA model. It was shown that the best ARIMA model for forecasting the urban population growth of the country is ARIMA (20, 1, 10). This model has been formulated and chosen through the mentioned statistical tests, and criteria for validation, and was further validated using error measures. The chosen ARIMA model was proven to be accurate based on the Root Mean Square Error (RMSE) of 0.18877 and the Mean Absolute Percentage Error (MAPE) of 3.71%. The researchers found an increase in the trend of 1.95% by 2022, 2.08% by 2024, 2.19% by 2026, and 2.36% by 2028. This potential rise in urban population growth in the Philippines may improve the economy of the country for the next 6 years, but this could also imply that the underlying issues of urbanization may get worse. The researchers conclude that the Philippine national government and local government units should have better and strengthened urban management and development programs to aid these problems. Government officials and even private sectors may use this paper as a reference to have an informed decision and policy-making. KEYWORDS: Autoregressive Integrated Moving Average (ARIMA) Model, Box-Jenkins Method, Urbanization, Urban Population Growth, Forecast, R Programming Language