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Journal articles on the topic 'Box-Jenkins ARIMA modeling'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

Al-Saati, Nabeel H., Isam I. Omran, Alaa Ali Salman, Zainab Al-Saati, and Khalid S. Hashim. "Statistical modeling of monthly streamflow using time series and artificial neural network models: Hindiya Barrage as a case study." Water Practice and Technology 16, no. 2 (2021): 681–91. http://dx.doi.org/10.2166/wpt.2021.012.

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Abstract Autoregressive Integrated Moving Average (ARIMA) Box-Jenkins models combine the autoregressive and moving average models to a stationary time series after the appropriate transformation, while the nonlinear autoregressive (N.A.R.) or the autoregressive neural network (ARNN) models are of the kind of multi-layer perceptron (M.L.P.), which compose an input layer, hidden layer and an output layer. Monthly streamflow at the downstream of the Euphrates River (Hindiya Barrage) /Iraq for the period January 2000 to December 2019 was modeled utilizing ARIMA and N.A.R. time series models. The p

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Upadhyay, Hari Prasad, and Bijay Lal Pradhan. "Forecasting GDP of Nepal using Autoregressive Integrated Moving Average (ARIMA) Model." International Journal of Silkroad Institute of Research and Training 1, no. 1 (2023): 2–7. http://dx.doi.org/10.3126/ijsirt.v1i1.55923.

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Background: Globally many research are working on modeling and forecasting of gross domestic product (GDP). The trend and pattern will help the planner and policy maker to make future monetary policy. The aim of this research is to find the ARIMA model and forecasting. Methods: Box-Jenkins methodology was use for the modeling and forecasting of annual GDP series of Nepal from 1990/91 to 2019/20. Eviews 10 software was use for data analysis. Results: Using the Box-Jenkins methodology this research examine the number of ARIMA family model that describe the annual GDP series and the a

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Echevarria, John Philip Omol, and Peter John Berces Aranas. "Forecasting the Consumer Price Index in the Regions of the Philippines using Machine Learning for Time Series Models." Journal of Artificial Intelligence, Machine Learning and Neural Network, no. 36 (September 13, 2023): 11–22. http://dx.doi.org/10.55529/jaimlnn.36.11.22.

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The core objective of this study is to showcase the enhanced forecasting capabilities of a hybrid model that combines the strengths of Artificial Neural Networks (ANN) and Autoregressive Integrated Moving Average (ARIMA) in predicting the Consumer Price Index (CPI). By harnessing the intricate non-linear pattern capturing ability of ANN and the capabilities of ARIMA in modeling linear and autoregressive components, the hybrid model aims to outperform the standalone ARIMA model in accurately forecasting the CPI. Real-world CPI data will be utilized for empirical evaluation and comparison, provi

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Pitaloka, Riski Arum, Sugito Sugito, and Rita Rahmawati. "PERBANDINGAN METODE ARIMA BOX-JENKINS DENGAN ARIMA ENSEMBLE PADA PERAMALAN NILAI IMPOR PROVINSI JAWA TENGAH." Jurnal Gaussian 8, no. 2 (2019): 194–207. http://dx.doi.org/10.14710/j.gauss.v8i2.26648.

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Import is activities to enter goods into the territory of a country, both commercial and non-commercial include goods that will be processed domestically. Import is an important requirement for industry in Central Java. The increase in high import values can cause deficit in the trade balance. Appropriate information about the projected amount of imports is needed so that the government can anticipate a high increase in imports through several policies that can be done. The forecasting method that can be used is ARIMA Box-Jenkins. The development of modeling in the field of time series forecas

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Inyang, E. J., E. P. Clement, M. A. Raheem, and E. M. Usungurua. "Non-seasonal ARIMA modeling of stroke incidence." World Journal of Applied Science & Technology 16, no. 1 (2025): 134–41. https://doi.org/10.4314/wojast.v16i1.134.

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This study employs the ARIMA Box-Jenkins methodology to model monthly stroke incidence data from January 2011 to December 2021. A non-seasonal ARIMA (p,d,q) model is fitted, with ARIMA (1,1,1) identified as the optimal model based on its lowest BIC (=781.5278) and AIC (=772.9022) values compared to alternative models. The Ljung-Box statistic (19.931, df = 22, p = 0.5885 > 0.05) affirms the model's suitability. Projections derived from the fitted model indicate a discernible trend towards increasing stroke incidence. Given the anticipated socioeconomic implications, particularly regarding em

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Chowdhury, Mashfiqul Huq, Somaresh Mondal, and Jobaidul Islam. "MODELING AND FORECASTING HUMIDITY IN BANGLADESH: BOX-JENKINS APPROACH." International Journal of Research -GRANTHAALAYAH 6, no. 4 (2018): 50–60. http://dx.doi.org/10.29121/granthaalayah.v6.i4.2018.1475.

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Humidity (atmospheric moisture) is an important atmospheric component and has significant influence on plant growth and development. The rate of growth and the form that a plant attains is controlled by humidity. The present study is an attempt to analyze the seasonal humidity’s of Bangladesh by employing appropriate statistical techniques. The main objective of this study is to examine humidity over time in Bangladesh and find a suitable model for forecasting. This study utilizes humidity data from Bangladesh Meteorological Department (BMD), recorded at 6 divisional meteorological stations fo

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Kadir, Dler. "Time Series Modeling to Forecast on Consuming Electricity: A case study Analysis of electrical consumption in Erbil City from 2014 to 2018." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 1 (October 1, 2021): 472–85. http://dx.doi.org/10.55562/jrucs.v46i1.98.

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Time series analysis and forecasting have become a major tool in different applications in hydrology and environmental management fields. Among the most effective approaches for analyzing time series data is the model introduced by Box and Jenkins, ARIMA (Autoregressive Integrated Moving Average). Approach: In this study, we used Box-Jenkins methodology to build ARIMA model for electricity consumption data taken for Erbil region station for the period from 2014-2018. Results: In this research, ARIMA (1, 1, 1) (0, 1, 1)12 model was developed. This model is used to forecasting the monthly consum

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Mahiyuddin, Wan Rozita Wan, Nur Izzah Jamil, Zamtira Seman, et al. "Forecasting Ozone Concentrations Using Box-Jenkins ARIMA Modeling in Malaysia." American Journal of Environmental Sciences 14, no. 3 (2018): 118–28. http://dx.doi.org/10.3844/ajessp.2018.118.128.

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Devkota, Bhim Prakash, and Sudip Pokhrel. "Forecasting Remittance Inflow in Nepal Using the Box-Jenkins ARIMA Model." Kathford Journal of Engineering and Management 3, no. 1 (2023): 72–86. http://dx.doi.org/10.3126/kjem.v3i1.62878.

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The remittance, defined as a portion of household income sent by individuals from their earnings in foreign economies, constitutes a substantial aspect of Nepal's current financial landscape. This research endeavors to identify an appropriate ARIMA model to forecast the remittance inflow in Nepal from 1990/91 to 2021/22. The Box-Jenkins methodology serves as the framework for modeling and forecasting the annual remittance inflow, with EViews 12 software employed for comprehensive data analysis. Various ARIMA models were evaluated to capture nuances in annual remittance trends. The investigatio

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Jafarian-Namin, Samrad, Alireza Goli, Mojtaba Qolipour, Ali Mostafaeipour, and Amir-Mohammad Golmohammadi. "Forecasting the wind power generation using Box–Jenkins and hybrid artificial intelligence." International Journal of Energy Sector Management 13, no. 4 (2019): 1038–62. http://dx.doi.org/10.1108/ijesm-06-2018-0002.

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Purpose The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria. Design/methodology/approach The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months. Findings The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power g

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D, Desi Fransiska. "Rainfall Data Modeling in Simalungun Regency Using the Arima Box-Jenkins Method." International Journal of Basic and Applied Science 10, no. 1 (2021): 21–27. http://dx.doi.org/10.35335/ijobas.v10i1.4.

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One of the components of the environment that determines the success of plant cultivation is climate. To predict rainfall, the author uses the ARIMA Box Jenkins method, which is a quantitative forecasting method. The data used are data for the period July 2012 to June 2017. In this study, the right model is the ARIMA model (2,0,2) with Xt = 4.05668 + 0.9416Xt-1 - 1.0039Xt-2 - 0, 8558et-1 + 0.9617et-2 + et which is used to forecast rainfall for the next 12 periods. The selection is based on the smallest MSE (average error squared) value of 0.033401954 and the smallest RMSE (root mean square err

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Mashfiqul, Huq Chowdhury 1., Kumar Mondal 1. Somaresh, and Islam *2 Jobaidul. "MODELING AND FORECASTING HUMIDITY IN BANGLADESH: BOXJENKINS APPROACH." International Journal of Research - Granthaalayah 6, no. 4 (2018): 50–61. https://doi.org/10.5281/zenodo.1241452.

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Humidity (atmospheric moisture) is an important atmospheric component and has significant influence on plant growth and development. The rate of growth and the form that a plant attains is controlled by humidity. The present study is an attempt to analyze the seasonal humidity’s of Bangladesh by employing appropriate statistical techniques. The main objective of this study is to examine humidity over time in Bangladesh and find a suitable model for forecasting. This study utilizes humidity data from Bangladesh Meteorological Department (BMD), recorded at 6 divisional meteorological stati

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Rahman, NMF, MM Hasan, MI Hossain, et al. "Forecasting Aus Rice Area and Production in Bangladesh using Box-Jenkins Approach." Bangladesh Rice Journal 20, no. 1 (2016): 1–10. http://dx.doi.org/10.3329/brj.v20i1.30623.

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Forecasting of rice area and production is an essential procedure in supporting policy decision regarding food security and environmental issues. The main aim of this paper is to forecast Aus rice area and production in Bangladesh. Box-Jenkins Autoregressive Integrated Moving Average (ARIMA) time-series methodology is considered for modeling and forecasting country's Aus rice area and production data commencing from 1971-72 to 2013-14. It was observed that ARIMA (1, 1, 5) and ARIMA (1, 1, 4) model were performed better than the other ARIMA models for forecasting Aus area and production respect

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HAZARIKA, J., B. PATHAK, and A. N. PATOWARY. "Studying monthly rainfall over Dibrugarh, Assam: Use of SARIMA approach." MAUSAM 68, no. 2 (2021): 349–56. http://dx.doi.org/10.54302/mausam.v68i2.637.

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Perceptive the rainfall pattern is tough for the solution of several regional environmental issues of water resources management, with implications for agriculture, climate change, and natural calamity such as floods and droughts. Statistical computing, modeling and forecasting data are key instruments for studying these patterns. The study of time series analysis and forecasting has become a major tool in different applications in hydrology and environmental fields. Among the most effective approaches for analyzing time series data is the ARIMA (Autoregressive Integrated Moving Average) model

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Chamalwa, Hamidu Aliyu, Bashir Abdulsalam, and Muhammad Abbas. "Modeling and Forecasting Crude Oil Prices in Nigeria Using ARIMA: A Time Series Analysis from 2013-2022." UMYU Scientifica 3, no. 3 (2024): 313–21. http://dx.doi.org/10.56919/usci.2433.033.

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Study’s Excerpt/Novelty This study employs the Box-Jenkins ARIMA model to forecast crude oil prices in Nigeria from 2013 to 2022, identifying the ARIMA (1, 1, 1) model as the most suitable based on AIC, BIC, and HQIC criteria. The findings emphasize the model’s adequacy for short-term price forecasting but highlight its limitations, particularly its sensitivity to non-stationary data. The study recommends incorporating macroeconomic variables or hybrid models for more accurate long-term predictions, while urging Nigerian oil sector stakeholders to explore alternatives like Liquefied Natural Ga

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Morales-Solis, Anabel, Artemio Pérez-López, Martha Elva Ramírez-Guzmán, Teodoro Espinosa-Solares, and Irán Alia-Tejacal. "ARIMAX Modelling: Response of Hass Avocado Respiration Rate to Environmental Factors." Horticulturae 10, no. 7 (2024): 700. http://dx.doi.org/10.3390/horticulturae10070700.

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This research explores how random events influence the respiration rate in Hass avocado beyond deterministic models in order to develop better strategies for extending its shelf life. Understanding these factors can enhance the accuracy of postharvest management strategies. The Autoregressive Integrated Moving Average (ARIMA) model with exogenous variables (ARIMAX) is an alternative stochastic probability model which is capable of modeling complex, externally influenced phenomena such as respiration. This study aimed to elucidate the effect of three exogenous variables, namely temperature, rel

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Agrienvi. "Frits Fahridws Damanik." Agrienvi, Jurnal Ilmu Pertanian 13, no. 02 (2020): 1–8. http://dx.doi.org/10.36873/aev.v13i02.657.

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ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean sothat must differenced whi

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Agrienvi. "DOI: https://doi.org/10.36873/ae , Frits Fahridws Damanik." Agrienvi: Jurnal Ilmu Pertanian 13, no. 02 (2020): 1–8. http://dx.doi.org/10.36873/aev.v13i02.723.

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ABSTRACTChili is one of the leading commodities of vegetables which has strategic value at national and regional levels.An unexpected increase in chili prices often results a surge of inflation and economic turmoil. Study and modeling ofchili production are needed as a planning and evaluation material for policy makers. One of the most frequently usedmethods in modeling and forecasting time series data is Autoregressive Integrated Moving Avarage (ARIMA). Theresults of ARIMA modeling on chili production data found that the data were unstationer conditions of the mean so thatmust differenced whi

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Maxwell, Obubu, Ikediuwa Udoka Chinedu, Anabike Charles Ifeanyi, and Nwokike Chukwudike C. "On Modeling Murder Crimes in Nigeria." Scientific Review, no. 58 (August 1, 2018): 157–62. http://dx.doi.org/10.32861/sr.58.157.162.

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This paper examines the modelling and forecasting Murder crimes using Auto-Regressive Integrated Moving Average models (ARIMA). Twenty-nine years data obtained from Nigeria Information Resource Center were used to make predictions. Among the most effective approaches for analyzing time series data is the method propounded by Box and Jenkins, the Autoregressive Integrated Moving Average (ARIMA). The augmented Dickey-Fuller test for unit root was applied to the data set to investigate for Stationarity, the data set was found to be non-stationary hence transformed using first-order differencing t

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Zamri, Muhammad Harith Ikhwan, Muhamad Afizi Rifin, and Amit Norani. "Application of the ARIMA model in house price index in Malaysia." Jurnal Intelek 19, no. 2 (2024): 184–92. http://dx.doi.org/10.24191/ji.v19i2.26615.

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The factors that affecting the escalating price of houses in Malaysia are driven by factors such as population growth, income dynamics, interest rates, and GDP. This phenomenon has notably outpaced the growth of household incomes, thus majorly impacting Malaysians. The study’s primary goal is to forecast the housing price index in Malaysia from the best model obtained using Box-Jenkins method. aligning with the 2018-2025 National Housing Policy objectives, utilizing advanced machine learning and time series modeling. The objectives guide the research: to find the best model for predicting hous

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Yin, Runsheng. "Forecasting Short-Term Timber Prices with Univariate ARIMA Models." Southern Journal of Applied Forestry 23, no. 1 (1999): 53–58. http://dx.doi.org/10.1093/sjaf/23.1.53.

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Abstract In this paper, we conduct timber price forecasts with univariate autoregressive-integrated-moving-average, or ARIMA, models employing the standard Box-Jenkins modeling strategy. Using quarterly price series from Timber Mart-South, we find that most of the selected pine pulpwood and sawtimber markets can be evaluated using ARIMA models, and that short-term forecasts, especially those of one-lead forecasts, are fairly accurate. We believe that forecasting future prices could aid timber producers and consumers alike in timing harvests, reducing uncertainty, and enhancing efficiency. Sout

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Guobadia, Emwinloghosa Kenneth, and Kenneth Kevin Uadiale. "Effect of Box-Cox Transformation on a k-th Weighted Moving Average Processes for Time Series." African Multidisciplinary Journal of Sciences and Artificial Intelligence 1, no. 1 (2024): 655–68. https://doi.org/10.58578/amjsai.v1i1.3755.

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In this paper, we examine, if the effect of transformation leads to improvement of model performance in time series modeling. The class of transformations that was considered is the Box-Cox family of transformation on the k-th weighted moving average (k-th WMA) model and autoregressive integrated moving average (ARIMA) model from a given nonstationary economic realization time series data. A real nonstationary economic time series data was used to demonstrate this procedure. The nonstationary time series data can be transformed to stationary data using the process of differencing alongside wit

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Sharma, Sapana, and Sanju Karol. "MODELING AND FORECASTING OF INDIA’S DEFENSE EXPENDITURES USING BOX-JENKINS ARIMA MODEL." International Journal of Research -GRANTHAALAYAH 9, no. 2 (2021): 334–44. http://dx.doi.org/10.29121/granthaalayah.v9.i2.2021.3698.

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Many developed and developing countries are at the core of the security and peace agenda concerning rising defense expenditure and its enduring sustainability. The unremitting upsurge in defense expenditure pressurizes the government to rationally manage the resources so as to provide security and peace services in the most efficient, effective and equitable way. It is necessary to forecast the defense expenditure in India which leads the policy makers to execute reforms in order to detract burdens on these resources, as well as introduce appropriate plan strategies on the basis of rational de

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Rumana, Majid, A. Mir Shakeel, Nazir Nageena, A. Wani Shabir, S. Pukhta M., and A. Wani Shafiq. "Statistical modeling and forecasting of Jammu and Kashmir apple prices." RESEARCH REVIEW International Journal of Multidisciplinary 4, no. 2 (2019): 267–74. https://doi.org/10.5281/zenodo.2574072.

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The Autoregressive integrated moving average model, developed by Box and Jenkins showed impressive and robust outcomes for forecasting market prices of agricultural products, finance and stock indices. In the present study, daily price of two apple varieties (super delicious and super american of Jammu and Kashmir, India) were studied for forecasting the market price. To address seasonality in the data series, a methodology called TBATS (Trigonometric, Box-Cox transform, ARMA errors, Trend, and Seasonal components) model was used. The TBATS model is helpful in capturing seasonality present at

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Rofel, Floria Sabenorio, Leonardo Enriquez Marivic, and Miguel Andres Ramel Lorenzo. "Forecasting Road Traffic Accidents in Metro Manila Using ARIMA Modeling." World Journal of Advanced Research and Reviews 17, no. 3 (2023): 115–25. https://doi.org/10.5281/zenodo.8123198.

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In this paper, we have determined and analyzed the behavior of road traffic accidents (RTAs) in Metro Manila, Philippines over the period of 2012-2021, and created a forecast for the next 5 years using ARIMA modeling. This study used 10-year historical monthly data collated through the Metro Manila Accident Recording and Analysis System program for the years 2012 through 2021. Our result suggests that the total RTAs in Metro Manila gradually increased until the first quarter of 2020, then it plummeted and reached its lowest point in April 2020 due to COVID-19 lockdown. As lockdown eases, it bo

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Chen, Yin Ping, Ai Ping Wu, Cui Ling Wang, Hai Ying Zhou, and Shu Xiu Feng. "Time Series Analysis of Pulmonary Tuberculosis Incidence: Forecasting by Applying the Time Series Model." Advanced Materials Research 709 (June 2013): 819–22. http://dx.doi.org/10.4028/www.scientific.net/amr.709.819.

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The main objective of this study is to identify the stochastic autoregressive integrated moving average (ARIMA) model to predict the pulmonary tuberculosis incidence in Qianan. Considering the Box-Jenkins modeling approach, the incidence of pulmonary tuberculosis was collected monthly from 2004 to 2010. The model ARIMA(0,1,1)12 was established finally and the residual sequence was a white noise sequence. Then, this model was used for calculating dengue incidence for the last 6 observations compared with observed data, and performed to predict the monthly incidence in 2011. It is necessary and

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Chandra, Ram Prasad. "Econometric Modeling for High Impact Sustainable Organic Tea Production: The Box-Jenkins Approach." Asian Journal of Economics, Business and Accounting 23, no. 24 (2023): 141–54. http://dx.doi.org/10.9734/ajeba/2023/v23i241193.

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India is world’s second leading tea producer, and biggest consumers in the worldwide. The main objective of this investigation is to identify the Box-Jenkins method an Autoregressive integrated moving average (ARIMA) models that can be used to make predictions the production and yield of tea in India. In this study considered the published yearly secondary data of tea production and yield in India period of 1970-71 to 2021-22. In accordance to the Sigma square, RMSE, MAPE, MAE, AIC and SIC the most appropriate models for prediction the tea production, and yield in India are ARIMA models (1, 1,

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Zulu, Julius, Gardner Mwansa, and Kenny Changwe. "Forecasting Inflation Rate Using the ARIMA Model: Zambia’s Perspective from 2023 to 2043." International Journal of Research and Scientific Innovation XI, no. XII (2025): 698–713. https://doi.org/10.51244/ijrsi.2024.11120063.

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The study sought to forecast Zambia’s inflation rate from 2023 to 2043 using the Autoregressive Integrated Moving Average Model (ARIMA). Using the Box–Jenkins modeling method, the study utilized 37 yearly time series data from 1986 to 2022 to forecast the next 20 years by using ARIMA Model. The ARIMA (4, 1, 2) model was used as being the one with the most significant parameters, the least log likelihood, Sigma, and the least Akaike and Bayesian information criteria. The ARIMA (4, 1, 2) model was also used due to its accuracy, mathematical soundness, and flexibility, thanks to the inclusion of

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C., Asogwa Oluchukwu, Eze C.M., and Okonkwo C. R. "On the Modelling of Road Traffic Crashes: A case of SARIMA Models." Journal of Advance Research in Mathematics And Statistics (ISSN: 2208-2409) 5, no. 8 (2018): 15–35. http://dx.doi.org/10.53555/nnms.v5i8.532.

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This paper examined the modeling of accident cases in four major roads leading to the main city of Enugu State of Nigeria using SARIMA Models. Among the most robust approaches for analysing time series data is the Autoregressive Integrated Moving Average (ARIMA) model propounded by Box and Jenkins (1979). In this paper, we employed the Box-Jenkins methodology to build SARIMA model for the accident cases for the period, January 2007 to December 2015 with a total of 108 data points. The model obtained in this paper was used to forecast monthly cases of accident in each of the roads for the upcom

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Shadab, A., S. Said, and S. Ahmad. "Box–Jenkins multiplicative ARIMA modeling for prediction of solar radiation: a case study." International Journal of Energy and Water Resources 3, no. 4 (2019): 305–18. http://dx.doi.org/10.1007/s42108-019-00037-5.

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Chandra, Ram Prasad, and Bhagawat Prasad Sahu. "Time Series Modeling and Forecasting of Finger Millet Cultivation Area, Production and Productivity in Chhattisgarh, India: The Box Jenkins Methodology." Asian Research Journal of Agriculture 17, no. 4 (2024): 18–30. http://dx.doi.org/10.9734/arja/2024/v17i4494.

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Chhattisgarh has taken important steps towards promoting millets cultivation and improving the livelihood of farmers. To increase millets production in Chhattisgarh, the state government launched the Millet Mission in September 2021. This mission has been started with a view to make Chhattisgarh the ‘millet hub of India’. The present study was conducted on time series modelling and forecasting of finger millet crops in Chhattisgarh India using Box Jenkins methodology and used historical data on currently cultivated area, production and yield of finger millet crops. The time series data was col

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Chirchir, Dan, Mirie Mwangi, and Cyrus Iraya. "Modeling Nairobi Residential Real Estate Prices using ARIMA." European Journal of Business and Management Research 9, no. 4 (2024): 30–36. http://dx.doi.org/10.24018/ejbmr.2024.9.4.2201.

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The residential real estate market is big and affords investors investment opportunities. The price changes are key in determining the overall return. Structural and atheoretical models are the two main approaches to modeling real estate prices. Structural models link prices to fundamental factors such as economic indicators and property supply, amongst others. Atheoretical models attempt to predict prices by leveraging on the statistical properties of time series data and may be extended to augment fundamental factors. This study focused on time series modeling using ARIMA. The objective of t

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Poudel, Bibas. "Predicting Cultivation Area, Production, and Yield of Maize in Nepal: An ARIMA Model Approach." Nepalese Journal of Statistics 8 (December 31, 2024): 61–78. https://doi.org/10.3126/njs.v8i1.73170.

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Background: Maize is the primary cereal crop of Nepal after rice. It is the major component of feed for the livestock and poultry sectors. The current maize yield is unable to meet its increasing demand in Nepal. Hence, substantial quantities of maize are being imported to fill the gap. Accurate forecasting of maize cultivation area, production, and yield is critical for successful market stabilization and sustainable agricultural practice promotion. Objective: The study aims to predict the cultivation area, production, and yield of maize in Nepal from 2023/24 to 2029/30 using appropriate Auto

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Xiang, Yuchen. "Forecasting the NASDAQ Index Based on the ARIMA Model." Advances in Economics, Management and Political Sciences 141, no. 1 (2024): 206–13. https://doi.org/10.54254/2754-1169/2024.ga18869.

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Forecasting the stock value is an enduring topic for investors. The ARIMA model is an effective model for forecasting proposed by Box and Jenkins in the 1970s. The NASDAQ Composite provides an index for investors to analyze the market of technical companies. Based on the ARIMA model, the author uses the NASDAQ Composite from Jan.2nd, 2020 to Jun.28th, 2024 for forecasting. This paper briefly introduces the NASDAQ Composite, ARIMA model and some judging criteria. The result of the modeling process reflects that the ARIMA (0, 1, 1) can better describe the index. The following actual value of the

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Chi, Yeong Nain, and Orson Chi. "Time Series forecasting global price of bananas using Hybrid ARIMA-NARNN model." Data Science in Finance and Economics 2, no. 3 (2022): 277–97. http://dx.doi.org/10.3934/dsfe.2022013.

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<abstract> <p>This study tried to demonstrate the role of Time Series models in a modeling and forecasting process using publicly available long-term records of monthly global price of bananas during the period of January 1990 to November 2021 reported in the International Monetary Fund. Following the Box–Jenkins methodology, an ARIMA (2,1,4) with a drift model was selected as the best-fit model for the Time Series, according to its lowest AIC value. Using the Levenberg-Marquardt algorithm, the results revealed that the NARNN model with 12 neurons in the hidden layer and 6 times de

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Boitan, Iustina Alina. "Residential property prices’ modeling: evidence from selected European countries." Journal of European Real Estate Research 9, no. 3 (2016): 273–85. http://dx.doi.org/10.1108/jerer-01-2016-0001.

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Purpose The purpose of this study is to contribute to the relatively narrow existing residential real estate literature by developing and validating several univariate forecasting models, to reliably anticipate future house price dynamics across several European Union (EU) countries. Design/methodology/approach The research approach relies on the time series analysis, by using the Box–Jenkins autoregressive integrated moving average (ARIMA) methodology to explore the trends of residential property prices in selected EU countries and to obtain a snapshot of the potential signs of change to be w

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Haque, Md Ariful, and Aziz Ahmed. "Time Series Modeling and Forecasting on GDP Data of Bangladesh: An Application of Arima Model." International Journal of Latest Technology in Engineering, Management & Applied Science XIII, no. IV (2024): 199–207. http://dx.doi.org/10.51583/ijltemas.2024.130423.

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The basic economic condition of a country is measured and presented by Gross Domestic Product (GDP). Government’s high officials, Business owners or managers, rely on forecasting of GDP, to determine fiscal year monitory policy and operating activities. This paper has collected GDP data from 1971 to 2021 from an international website. Exploratory analysis has performed for trend, seasonality & outlier detection. Augmented Dickey-Fuller test, Phillip Peron and Kwiatkowski-Phillips-Schmidt-Shin test has performed to check seasonality and stationary. To determine the best fitted model for GDP

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Shafie, Nur Amalina, Nurbaizura Borhan, Noor Amalina Nisa Ariffin, Khairil Anuar Md Isa, and Nor Azura Md Ghani. "Modeling and Forecasting of Total Supply Malaysia’s Centrifugal Sugar Using ARIMA Model." Advances in Social Sciences Research Journal 11, no. 11 (2024): 238–46. https://doi.org/10.14738/assrj.1111.17941.

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Centrifugal sugar is essential to Malaysia's food sector and culinary scene because it is high-quality, consistent, and convenient for a variety of food and beverage applications. Customers, chefs, and food manufacturers who are looking for dependable sweetening solutions for their regular cooking and dining experiences choose it because of its broad availability and adaptability. To support market stability, price control, production scheduling, trade policy development, risk management, policy formation, and consumer welfare, it is essential to forecast the centrifugal sugar supply in Malays

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Stadnytska, Tetiana, and Joachim Werner. "Sample Size and Accuracy of Estimation of the Fractional Differencing Parameter." Methodology 2, no. 4 (2006): 135–41. http://dx.doi.org/10.1027/1614-2241.2.4.135.

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Recent empirical studies on human performance in cognitive tasks have provided evidence of long-range dependence in psychological time series. ARFIMA (p, d, q) methodology, an extension of the traditional Box-Jenkins ARIMA modeling, allows estimation of the long-term dependence in the presence of any possible short-memory components. This article examines, by means of Monte Carlo experiments, sample size requirements for the accurate estimation of the long-memory parameter d and documents the quality of the estimates for time series of different length in various (0, d, 0) and (1, d, 1) models

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Rakhmawati, Desty, and Eka Tripustikasari. "IMPLEMENTASI METODE BOX-JENKINS UNTUK MEMPREDIKSI HARGA MINYAK DUNIA DAN PENGARUHNYA TERHADAP HARGA MINYAK INDONESIA." Jurnal Ilmiah Matematika dan Pendidikan Matematika 9, no. 2 (2017): 87. http://dx.doi.org/10.20884/1.jmp.2017.9.2.2869.

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Recently Indonesia's oil prices have declined despite oil consumption increases. The decline in Indonesia oil prices is affected by world oil prices. Therefore, the aim of this research is to predict WTI-type world oil price using Box-Jenkins method for non seasonal ARIMA modeling and how big does it influence to Indonesia oil price. WTI world oil price predictions for January to December 2017 are 54.17035, 54.89475, 55.12885, 55.20406, 55.22817, 55.23590, 55.23837, 55.23916, 55.23942, 55.23950, 55.23953, and 55.23953 dollars per barrels, respectively. Then the magnitude of the effect of WTI w

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Chi, Yeong Nain. "Time Series Modeling of Global Average Absolute Sea Level Change." American Journal of Environment and Climate 2, no. 3 (2023): 81–90. http://dx.doi.org/10.54536/ajec.v2i3.2093.

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This study aimed to demonstrate the effectiveness of time series models in modeling long-term records of global average absolute sea level changes from 1880 to 2014. Following the Box–Jenkins methodology, the ARIMA(0,1,2) model with drift was identified as the best-fit model for the time series due to its lowest AIC value. Using the LM algorithm, the results revealed that the NARNN model with 7 neurons in the hidden layer and 7 time delays exhibited the best performance among the nonlinear autoregressive neural network models, as indicated by its lower MSE. While ARIMA models excel in modeling

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Bărbulescu, Alina, and Cristian Ștefan Dumitriu. "Modeling the Voltage Produced by Ultrasound in Seawater by Stochastic and Artificial Intelligence Methods." Sensors 22, no. 3 (2022): 1089. http://dx.doi.org/10.3390/s22031089.

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Experiments have proved that an electrical signal appears in the ultrasonic cavitation field; its properties are influenced by the ultrasound frequency, the liquid type, and liquid characteristics such as density, viscosity, and surface tension. Still, the features of the signals are not entirely known. Therefore, we present the results on modeling the voltage collected in seawater, in ultrasound cavitation produced by a 20 kHz frequency generator, working at 80 W. Comparisons of the Box–Jenkins approaches, with artificial intelligence methods (GRNN) and hybrid (Wavelet-ARIMA and Wavelet-ANN)

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M. K., SHARMA, OMER MOHAMMED, and KIANI SARA. "Time series analysis on precipitation with missing data using stochastic SARIMA." MAUSAM 71, no. 4 (2021): 617–24. http://dx.doi.org/10.54302/mausam.v71i4.45.

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This paper presents an application of the Box-Jenkins methodology for modeling the precipitation in Iran. Linear stochastic model known as multiplicative seasonal ARIMA was used to model the monthly precipitation data for 44 years. Missing data occurred in between for 34 months for some reason. To fill the gap a SARIMA model was fitted based on the first 180 available observations and the missing observations were substituted by the forecasts for the next 34 months. Then a SARIMA model was fitted for the full data. The result showed that the fitted model represent the full data well.

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Abd Al-zahra, Khadeega, Khulood Moosa, and Basil Jasim. "A comparative Study of Forecasting the Electrical Demand in Basra city using Box-Jenkins and Modern Intelligent Techniques." Iraqi Journal for Electrical and Electronic Engineering 11, no. 1 (2015): 110–23. http://dx.doi.org/10.37917/ijeee.11.1.12.

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The electrical consumption in Basra is extremely nonlinear; so forecasting the monthly required of electrical consumption in this city is very useful and critical issue. In this Article an intelligent techniques have been proposed to predict the demand of electrical consumption of Basra city. Intelligent techniques including ANN and Neuro-fuzzy structured trained. The result obtained had been compared with conventional Box-Jenkins models (ARIMA models) as a statistical method used in time series analysis. ARIMA (Autoregressive integrated moving average) is one of the statistical models that ut

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Hussain, Muhammad Hammad. "Navigating Pakistan's Export Future: Forecasting Trends Using ARIMA." Research Letters 2, no. 1 (2024): 47–56. https://doi.org/10.5281/zenodo.13917447.

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This study aims to forecast Pakistan's exports from 2024 to 2030 using historical data from 1990 to 2023. The data is providing a comprehensive view of Pakistan’s export trends. The Box-Jenkins methodology was employed, utilizing the ARIMA model for time series forecasting. Prior to modeling, a unit root test was conducted to assess stationarity, which revealed that the export data was non-stationary at its level but became stationary after first differencing. The ARIMA model forecasts an initial increase in exports in the next two years, followed by a decline from 2026 to 2030. This tre

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Tran, Thai Thanh, Luong Duc Thien, Ngo Xuan Quang, and Lam Van Tan. "Forecasting of saline intrusion in Ham Luong river, Ben Tre province (Southern Vietnam) using Box-Jenkins ARIMA models." Science and Technology Development Journal 23, no. 1 (2020): 446–53. http://dx.doi.org/10.32508/stdj.v23i1.1747.

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Introduction: Ham Luong River is a branch of Mekong River located in Ben Tre Province, which has played a crucial role in supporting livelihoods of local residents and the province's economic development. However, the saline intrusion has been expanding in Ham Luong River, which seriously affects the productive agriculture, aquaculture, and further causes tremendous difficulties for local people's lives. Thus, it is crucial to have research for forecast the saline intrusion in Ham Luong River. Our aim was to develop mathematical models in order to forecast the saline intrusion in Ham Luong Riv

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Ho, S. L., M. Xie, and T. N. Goh. "A comparative study of neural network and Box-Jenkins ARIMA modeling in time series prediction." Computers & Industrial Engineering 42, no. 2-4 (2002): 371–75. http://dx.doi.org/10.1016/s0360-8352(02)00036-0.

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Rofel Floria Sabenorio, Marivic Leonardo Enriquez, and Lorenzo Miguel Andres Ramel. "Forecasting Road Traffic Accidents in Metro Manila Using ARIMA Modeling." World Journal of Advanced Research and Reviews 17, no. 3 (2023): 115–25. http://dx.doi.org/10.30574/wjarr.2023.17.3.0337.

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In this paper, we have determined and analyzed the behavior of road traffic accidents (RTAs) in Metro Manila, Philippines over the period of 2012-2021, and created a forecast for the next 5 years using ARIMA modeling. This study used 10-year historical monthly data collated through the Metro Manila Accident Recording and Analysis System program for the years 2012 through 2021. Our result suggests that the total RTAs in Metro Manila gradually increased until the first quarter of 2020, then it plummeted and reached its lowest point in April 2020 due to COVID-19 lockdown. As lockdown eases, it bo

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Thanoon, Shaymaa Riyadh. "Modeling Time Series for Prediction of Thalassemia in Nineveh Governorate." Samarra Journal of Pure and Applied Science 2, no. 3 (2021): 120–31. http://dx.doi.org/10.54153/sjpas.2020.v2i3.30.

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The aim of this research is to analyze the time series of Thalassemia cancer cases by making assumptions on the number of cases to formulate the problem to find the best model for predicting the number of patients in Nineveh governorate using (Box and Jenkins) method of analysis based on the monthly data provided by Al Salam Hospital in Nineveh for the period (2014-2018). The results of the analysis showed that the appropriate model of analysis is the Auto-Regressive Integrated Moving Average (ARIMA) (2,1,0) and based on this model the number of people with this disease was predicted for the n

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Poudel, Omkar, Khom Raj Kharel, Pradeep Acharya, Daya Simkhada, and Sarad Chandra Kafle. "ARIMA Modeling and Forecasting of National Consumer Price Index in Nepal." Interdisciplinary Journal of Management and Social Sciences 5, no. 1 (2024): 105–18. http://dx.doi.org/10.3126/ijmss.v5i1.62666.

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This research aims to determine an Auto-Regressive Integrated Moving Average (ARIMA) model and forecasting the National Consumer Price Index (NCPI) in Nepal, using annual data from the fiscal year 1972/73 to 2022/23. The research utilized secondary data collected from the online bulletin published by Nepal Rastra Bank, aiming to enhance understanding of NCPI patterns and contribute to predictive modelling techniques for economic indicators. Adopting the Box-Jenkins technique and using E-Views statistical software, this study identifies ARIMA (1, 2, 8) as the most suitable model for NCPI foreca

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