Relevant bibliographies by topics / Box-Jenkins methods

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Box-Jenkins methods'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Box-Jenkins methods"

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Jin, Chenhao. "Forecasting Models for Apple Inc. Stock Price Using Regression Smoothing and Box Jenkins Time Series Analysis." Science, Technology and Social Development Proceedings Series 2 (November 9, 2024): 77–87. http://dx.doi.org/10.70088/t3ar0344.

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In financial markets, stock price forecasting plays a critical role in investment decision-making, especially for globally influential companies like Apple Inc. This study aims to develop and assess models for predicting Apple Inc.'s stock price using various approaches, including regression analysis, smoothing methods, and the Box-Jenkins methodology. We analyzed ten years of Apple Inc.'s historical adjusted closing price data to construct models such as unregularized regression, regularized regression (Ridge and Lasso), smoothing methods (including exponential smoothing and moving averages),
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Poměnková, Jitka. "USA business cycle identification – a comparative study of chosen methods." Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis 55, no. 6 (2007): 125–32. http://dx.doi.org/10.11118/actaun200755060125.

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Presented paper deals with comparison of chosen methods used for the business cycle identification. With respect to this aim nonparametric method (kernel smoothing) and Box-Jenkins methodology were used. This comparison is performed by application on economic activity in USA 1960/Q01–2007/Q01. The residuals are tested by Box-Pierce test. Identified trend is discussed with chosen historical events which affect business cycle in the USA.
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Kuru, Merve, and Gulben Calis. "Application of time series models for heating degree day forecasting." Organization, Technology and Management in Construction: an International Journal 12, no. 1 (2020): 2137–46. http://dx.doi.org/10.2478/otmcj-2020-0009.

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AbstractThis study aims at constructing short-term forecast models by analyzing the patterns of the heating degree day (HDD). In this context, two different time series analyses, namely the decomposition and Box–Jenkins methods, were conducted. The monthly HDD data in France between 1974 and 2017 were used for analyses. The multiplicative model and 79 SARIMA models were constructed by the decomposition and Box–Jenkins method, respectively. The performance of the SARIMA models was assessed by the adjusted R2 value, residual sum of squares, the Akaike Information Criteria, the Schwarz Informatio
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Marriott, John. "Bayesian Numerical and Graphical Methods for Box-Jenkins Time Series." Statistician 36, no. 2/3 (1987): 265. http://dx.doi.org/10.2307/2348522.

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Maheshnath, M., R. Vijaya Kumari, K. Suhasini, D. Srinivasa Reddy, and A. Meena. "Forecasting Maize Production in Telangana State Using Arima Model." Archives of Current Research International 24, no. 6 (2024): 223–29. http://dx.doi.org/10.9734/acri/2024/v24i6780.

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The study utilized the Box-Jenkins approach for forecasting maize production in Telangana state. It involved the analysis of 55 years of empirical annual observations of maize production. The autocorrelation (ACF) and partial autocorrelation functions (PACF) were calculated to analyze the data. A suitable Box-Jenkins ARIMA model was fitted, and the validity of the model was examined using conventional statistical methods. Lastly, the next three years' worth of maize production was predicted using the autoregressive integrated moving average model's forecasting capability.
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Abu, Noratikah, Siti Aishah @Tsamienah Taib, Nurul Amira Zainal, Nor Azuana Ramli, and Clark Kendrick Go. "TIME SERIES FORECASTING FOR TOURISM INDUSTRY IN MALAYSIA." Advances and Applications in Statistics 92, no. 1 (2024): 77–87. http://dx.doi.org/10.17654/0972361725004.

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This study is conducted to forecast the future tourism demand in Malaysia by applying Box-Jenkins modelling. The time series data of tourist arrivals volume in Malaysia before MCO retrieved from MOTAC Malaysia database is implemented in this study. The forecast evaluation methods used to validate the best Box-Jenkins model before proceeding to forecasting stage are MAPE and RMSE, and the analysis was performed by using Python. The findings show that SARIMA was considered as highly accurate forecasting model based on its least error produced.
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Banditvilai, Somsri, and Autcha Araveeporn. "Empirical Comparison of Forecasting Methods for Air Travel and Export Data in Thailand." Modelling 5, no. 4 (2024): 1395–412. http://dx.doi.org/10.3390/modelling5040072.

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Time series forecasting plays a critical role in business planning by offering insights for a competitive advantage. This study compared three forecasting methods: the Holt–Winters, Bagging Holt–Winters, and Box–Jenkins methods. Ten datasets exhibiting linear and non-linear trends and clear and ambiguous seasonal patterns were selected for analysis. The Holt–Winters method was tested using seven initial configurations, while the Bagging Holt–Winters and Box–Jenkins methods were also evaluated. The model performance was assessed using the Root-Mean-Square Error (RMSE) to identify the most effec
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Laurain, Vincent, Marion Gilson, Roland Tóth, and Hugues Garnier. "Refined instrumental variable methods for identification of LPV Box–Jenkins models." Automatica 46, no. 6 (2010): 959–67. http://dx.doi.org/10.1016/j.automatica.2010.02.026.

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Ding, Feng, Dandan Meng, and Qi Wang. "The model equivalence based parameter estimation methods for Box–Jenkins systems." Journal of the Franklin Institute 352, no. 12 (2015): 5473–85. http://dx.doi.org/10.1016/j.jfranklin.2015.08.018.

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Purnawansyah, Purnawansyah, Haviluddin Haviluddin, Rayner Alfred, and Achmad Fanany Onnilita Gaffar. "Network Traffic Time Series Performance Analysis Using Statistical Methods." Knowledge Engineering and Data Science 1, no. 1 (2017): 1. http://dx.doi.org/10.17977/um018v1i12018p1-7.

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This paper presents an approach for a network traffic characterization by using statistical techniques. These techniques are obtained using the decomposition, winter’s exponential smoothing and autoregressive integrated moving average (ARIMA). In this paper, decomposition and winter’s exponential smoothing techniques were used additive and multiplicative model. Then, ARIMA based-on Box-Jenkins methodology. The results of ARIMA (1,0,2) was shown the best model that can be used to the internet network traffic forecasting.
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Dissertations / Theses on the topic "Box-Jenkins methods"

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Simmons, Laurette Poulos. "The Development and Evaluation of a Forecasting System that Incorporates ARIMA Modeling with Autoregression and Exponential Smoothing." Thesis, North Texas State University, 1985. https://digital.library.unt.edu/ark:/67531/metadc332047/.

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This research was designed to develop and evaluate an automated alternative to the Box-Jenkins method of forecasting. The study involved two major phases. The first phase was the formulation of an automated ARIMA method; the second was the combination of forecasts from the automated ARIMA with forecasts from two other automated methods, the Holt-Winters method and the Stepwise Autoregressive method. The development of the automated ARIMA, based on a decision criterion suggested by Akaike, borrows heavily from the work of Ang, Chuaa and Fatema. Seasonality and small data set handling were some
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Zhao, Tao. "A new method for detection and classification of out-of-control signals in autocorrelated multivariate processes." Morgantown, W. Va. : [West Virginia University Libraries], 2008. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5615.

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Thesis (M.S.)--West Virginia University, 2008.<br>Title from document title page. Document formatted into pages; contains x, 111 p. : ill. Includes abstract. Includes bibliographical references (p. 102-106).
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Šimpach, Ondřej. "Statistické metody v demografickém prognózování." Doctoral thesis, Vysoká škola ekonomická v Praze, 2013. http://www.nusl.cz/ntk/nusl-203730.

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Dissertation thesis creates a complex and modern scheme for stochastic modeling of demographic processes, which is universally applicable to any population in the world. All calculations are described in detail on the data of the Czech Republic. Throughout the work the attention is drawn to the issues, that every analyst must necessarily take into account in order to obtain correct results. Data comes mostly from the Czech Statistical Office database. However, some data matrices had to be calculated for the purposes of the thesis. Particular demographic processes (mortality, fertility and migr
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Tang, Kim Oanh. "Projection de la mortalité aux âges avancées au Canada : comparaison de trois modèles." Thèse, 2009. http://hdl.handle.net/1866/7899.

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Makananisa, Mangalani P. "Forecasting annual tax revenue of the South African taxes using time series Holt-Winters and ARIMA/SARIMA Models." Diss., 2015. http://hdl.handle.net/10500/19903.

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This study uses aspects of time series methodology to model and forecast major taxes such as Personal Income Tax (PIT), Corporate Income Tax (CIT), Value Added Tax (VAT) and Total Tax Revenue(TTAXR) in the South African Revenue Service (SARS). The monthly data used for modeling tax revenues of the major taxes was drawn from January 1995 to March 2010 (in sample data) for PIT, VAT and TTAXR. Due to higher volatility and emerging negative values, the CIT monthly data was converted to quarterly data from the rst quarter of 1995 to the rst quarter of 2010. The competing ARIMA/SARIMA and Holt-W
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Luruli, Fululedzani Lucy. "ARIMA forecasts of the number of beneficiaries of social security grants in South Africa." Diss., 2011. http://hdl.handle.net/10500/5810.

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The main objective of the thesis was to investigate the feasibility of accurately and precisely fore- casting the number of both national and provincial bene ciaries of social security grants in South Africa, using simple autoregressive integrated moving average (ARIMA) models. The series of the monthly number of bene ciaries of the old age, child support, foster care and disability grants from April 2004 to March 2010 were used to achieve the objectives of the thesis. The conclusions from analysing the series were that: (1) ARIMA models for forecasting are province and grant-type spe- c
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Books on the topic "Box-Jenkins methods"

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Geōrganta, Zōē. Hē prosengisē Box-Jenkins stēn analysē kai provlepsē chronologikōn seirōn. Kentro Programmatismou kai Oikonomikōn Ereunōn, 1987.

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Werner, Fuchs. Box-Jenkins-Prognosen der kurzfristigen Produktionsentwicklung: Dargestellt am Beispiel ausgewählter Branchen des verarbeitenden Gewerbes in der Bundesrepublik Deutschland zwischen 1976 und 1985. J. Eul, 1989.

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B, Hudak Gregory, ed. Forecasting and time series analysis using the SCA statistical system: Vol. I : Box-Jenkins ARIMA modeling, intervention analysis, transfer function modeling, outlier detection and adjustment, exponential smoothing, related univariate methods. Scientific computing associates corp., 1992.

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Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1079837.

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The textbook includes topics of modern econometrics, often used in economic research. Some aspects of multiple regression models related to the problem of multicollinearity and models with a discrete dependent variable are considered, including methods for their estimation, analysis, and application. A significant place is given to the analysis of models of one-dimensional and multidimensional time series. Modern ideas about the deterministic and stochastic nature of the trend are considered. Methods of statistical identification of the trend type are studied. Attention is paid to the evaluati
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McCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

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Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial a
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McCleary, Richard, David McDowall, and Bradley Bartos. Design and Analysis of Time Series Experiments. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.001.0001.

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Design and Analysis of Time Series Experiments develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioral, and biomedical time series illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. The classic Box-Jenkins-Tiao model-building strategy is supplemented with recent auxiliary tests for transformation, differencing, and model selection. The validity of causal inferences is approached from two complementary directions. The four-validity system of Cook and Campbell
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Evaluation mit Hilfe der Box-Jenkins-Methode: Eine Untersuchung zur Überprüfung der Wirksamkeit einer legislativen Massnahme zur Erhöhung der richterlichen Arbeitseffektivität im Bereich der Zivilgerichtsbarkeit. P. Lang, 1986.

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McDowall, David, Richard McCleary, and Bradley J. Bartos. Interrupted Time Series Analysis. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190943943.001.0001.

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Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioural, and biomedical time series illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. The classic Box-Jenkins-Tiao model-building strategy is supplemented with recent auxiliary tests for transformation, differencing and model selection. New developments, including Bayesian hypothesis testing and synthetic control group designs are described and their prospects for widespread a
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Book chapters on the topic "Box-Jenkins methods"

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Aljandali, Abdulkader. "The Box-Jenkins Methodology." In Multivariate Methods and Forecasting with IBM® SPSS® Statistics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56481-4_3.

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Laurain, Vincent, Marion Gilson, and Hugues Garnier. "Refined Instrumental Variable Methods for Hammerstein Box-Jenkins Models." In System Identification, Environmental Modelling, and Control System Design. Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-974-1_2.

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Milionis, Alexandros E. "Box Jenkins Multivariate Modelling and Co-Integration: Two Statistical Methods with Potential Usefulness in Climatic Studies." In Long-Term Climatic Variations. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-79066-9_18.

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Xie, Li, Huizhong Yang, and Feng Ding. "Interactive Identification Method for Box-Jenkins Models." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15859-9_23.

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Jatiningrum, D., C. C. de Visser, M. M. van Paassen, E. van Kampen, and M. Mulder. "Characterising Angular Accelerometer Calibration Setup Disturbance Using Box–Jenkins Method." In Advances in Aerospace Guidance, Navigation and Control. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65283-2_15.

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Baláte, Jaroslav, and Bronislav Chramcov. "Selection of Mathematics Model for Daily Diagram Prediction According to Box-Jenkins Method." In Fuzzy Control. Physica-Verlag HD, 2000. http://dx.doi.org/10.1007/978-3-7908-1841-3_21.

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Agustriyanto, Rudy, P. Setyopratomo, and E. Srihari Mochni. "The Application of the Box-Jenkins (BJ) Method for Process Identification of the Batch Milk Cooling System." In Atlantis Highlights in Engineering. Atlantis Press International BV, 2023. http://dx.doi.org/10.2991/978-94-6463-288-0_6.

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Allafi, Walid, and Keith J. Burnham. "Identification of Fractional-Order Continuous-Time Hybrid Box-Jenkins Models Using Refined Instrumental Variable Continuous-Time Fractional-Order Method." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-01857-7_75.

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McDowall, David, Richard McCleary, and Bradley J. Bartos. "The Noise Component:N(at)." In Interrupted Time Series Analysis. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190943943.003.0003.

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Chapter 3 develops the methods or strategies for building ARIMA noise models. At one level, the iterative identify-estimate-diagnose modeling strategy proposed by Box and Jenkins has changed little. At another level, the collective experience of time series experimenters leads to several modifications of the strategy. For the most part, these changes are aimed at solving practical problems. Compared to the 1970s, for example, modelers today pay more attention to transformations and to the usefulness and interpretability of an ARIMA model. The Box-Jenkins ARIMA noise modeling strategy is illust
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Hylleberg, S. "Introduction." In Modelling Seasonality. Oxford University PressOxford, 1992. http://dx.doi.org/10.1093/oso/9780198773177.003.0014.

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Abstract The applications of very complex seasonal adjustment procedures like the X-11 which are based on more or less ad hoc methods have left many statisticians working with time series uneasy for several reasons. First, the complex nature of the procedures makes it virtually impossible to evaluate the procedures except by looking at the output and input series. Secondly, the extensive use of moving average filters causes endpoint problems which in turn imply often large revisions of the seasonally adjusted data. Thirdly, the black-box nature of the procedure prevents a deeper understanding
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Conference papers on the topic "Box-Jenkins methods"

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Li, Gong, and Jing Shi. "Comparison of Different Time Series Methods for Short-Term Forecasting of Wind Power Production." In ASME 2010 4th International Conference on Energy Sustainability. ASMEDC, 2010. http://dx.doi.org/10.1115/es2010-90270.

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Reliable short-term predictions of the wind power production are critical for both wind farm operations and power system management, where the time scales can vary in the order of several seconds, minutes, hours and days. This comprehensive study mainly aims to quantitatively evaluate and compare the performances of different Box &amp; Jenkins models and backpropagation (BP) neural networks in forecasting the wind power production one-hour ahead. The data employed is the hourly power outputs of an N.E.G. Micon 900-kilowatt wind turbine, which is installed to the east of Valley City, North Dako
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Laurain, V., M. Gilson, H. Garnier, and P. C. Young. "Refined instrumental variable methods for identification of Hammerstein continuous-time Box-Jenkins models." In 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4738853.

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Laurain, V., M. Gilson, R. Toth, and H. Garnier. "Identification of LPV output-error and Box-Jenkins models via optimal refined instrumental variable methods." In 2010 American Control Conference (ACC 2010). IEEE, 2010. http://dx.doi.org/10.1109/acc.2010.5530665.

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Essallah, Sirine, Adel Bouallegue, and Adel Khedher. "Performance evaluation of Box-Jenkins and linear-regressions methods versus the study-period's variations: Tunisian grid case." In 2015 12th International Multi-Conference on Systems, Signals & Devices (SSD). IEEE, 2015. http://dx.doi.org/10.1109/ssd.2015.7348153.

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Aimran, Nazim, Asyraf Afthanorhan, Anis Ishak, Dina Asmadi, Naufil Jamaludin, and Nurbaizura Borhan. "A comparison between the exponential smoothing, methods of average and Box Jenkins techniques in forecasting the ageing population in Malaysia." In PROCEEDINGS OF THE 38TH INTERNATIONAL CONFERENCE OF THE POLYMER PROCESSING SOCIETY (PPS-38). AIP Publishing, 2024. http://dx.doi.org/10.1063/5.0223872.

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Shakir, Huzefa, and Won-Jong Kim. "Discrete-Time Closed-Loop Model Identification of Fixed-Structure Unstable Multivariable Systems." In ASME 2007 International Mechanical Engineering Congress and Exposition. ASMEDC, 2007. http://dx.doi.org/10.1115/imece2007-41834.

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This paper presents improved empirical representations of a general class of open-loop unstable systems using closed-loop system identification. A multi-axis magnetic-levitation (maglev) nanopositioning system with an extended translational travel range is used as a test bed to verify the closed-loop system-identification method proposed in this paper. A closed-loop identification technique employing the Box-Jenkins (BJ) method and a known controller structure is developed for model identification and validation. Direct and coupling transfer functions (TFs) are then derived from the experiment
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Victor, Ste´phane, Rachid Malti, and Alain Oustaloup. "Instrumental Variable Method for Identifying Fractional Box-Jenkins Models." In ASME 2009 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/detc2009-86984.

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This paper deals with continuous-time system identification using fractional differentiation models in a colored noisy output context. An optimal instrumental variable method for identifying hybrid fractional Box-Jenkins models is described. The relationship between the measured input and the output is a fractional continuous-time transfer function, and the noise is a discrete-time AR or ARMA process. The method is illustrated on a simulation example.
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Ismail, Zuhaimy, Mohd Zulariffin Md Maarof, and Mohammad Fadzli. "Alteration of Box-Jenkins methodology by implementing genetic algorithm method." In THE 2ND ISM INTERNATIONAL STATISTICAL CONFERENCE 2014 (ISM-II): Empowering the Applications of Statistical and Mathematical Sciences. AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4907522.

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Abu, Noratikah, and Zuhaimy Ismail. "A study on private vehicle demand forecasting based on Box-Jenkins method." In PROCEEDINGS OF THE 3RD INTERNATIONAL CONFERENCE ON AUTOMOTIVE INNOVATION GREEN ENERGY VEHICLE: AIGEV 2018. Author(s), 2019. http://dx.doi.org/10.1063/1.5085948.

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Arshad, Maryam, and Naiza Arshad. "Forecasting Of Different Crops by Area Of Punjab Using Box-Jenkins Method." In Resent Trends in Statistics Data Analytics. Air University, 2024. https://doi.org/10.62500/icrtsda.1.1.17.

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