Relevant bibliographies by topics / BOX-JENKINS

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

Academic literature on the topic 'BOX-JENKINS'

Author: Grafiati
Published: 4 June 2021
Last updated: 4 February 2022

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Journal articles on the topic "BOX-JENKINS"

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Kremer, Erhard. "Box-Jenkins credibility." Blätter der DGVFM 18, no. 4 (October 1988): 277–89. http://dx.doi.org/10.1007/bf02808821.

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Pepper, M. P. G. "Multivariate Box-Jenkins analysis." Energy Economics 7, no. 3 (July 1985): 168–78. http://dx.doi.org/10.1016/0140-9883(85)90006-4.

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Pintelon, R., J. Schoukens, and Y. Rolain. "Box-Jenkins Continuous-Time Modeling." IFAC Proceedings Volumes 33, no. 15 (June 2000): 193–98. http://dx.doi.org/10.1016/s1474-6670(17)39749-5.

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Lu, Y., and S. M. AbouRizk. "Automated Box–Jenkins forecasting modelling." Automation in Construction 18, no. 5 (August 2009): 547–58. http://dx.doi.org/10.1016/j.autcon.2008.11.007.

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Pintelon, R., J. Schoukens, and Y. Rolain. "Box–Jenkins continuous-time modeling." Automatica 36, no. 7 (July 2000): 983–91. http://dx.doi.org/10.1016/s0005-1098(00)00002-9.

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Khalfi, Jaouad, Najib Boumaaz, Abdallah Soulmani, and El Mehdi Laadissi. "Box–Jenkins Black-Box Modeling of a Lithium-Ion Battery Cell Based on Automotive Drive Cycle Data." World Electric Vehicle Journal 12, no. 3 (July 28, 2021): 102. http://dx.doi.org/10.3390/wevj12030102.

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The Box–Jenkins model is a polynomial model that uses transfer functions to express relationships between input, output, and noise for a given system. In this article, we present a Box–Jenkins linear model for a lithium-ion battery cell for use in electric vehicles. The model parameter identifications are based on automotive drive-cycle measurements. The proposed model prediction performance is evaluated using the goodness-of-fit criteria and the mean squared error between the Box–Jenkins model and the measured battery cell output. A simulation confirmed that the proposed Box–Jenkins model could adequately capture the battery cell dynamics for different automotive drive cycles and reasonably predict the actual battery cell output. The goodness-of-fit value shows that the Box–Jenkins model matches the battery cell data by 86.85% in the identification phase, and 90.83% in the validation phase for the LA-92 driving cycle. This work demonstrates the potential of using a simple and linear model to predict the battery cell behavior based on a complex identification dataset that represents the actual use of the battery cell in an electric vehicle.
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Helfenstein, Ulrich. "Box-Jenkins modelling in medical research." Statistical Methods in Medical Research 5, no. 1 (March 1996): 3–22. http://dx.doi.org/10.1177/096228029600500102.

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Pintelon, R., P. Guillaume, and J. Schoukens. "MULTIVARIABLE FREQUENCY DOMAIN BOX-JENKINS IDENTIFICATION." IFAC Proceedings Volumes 39, no. 1 (2006): 208–13. http://dx.doi.org/10.3182/20060329-3-au-2901.00027.

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Piga, Dario, Valentina Breschi, and Alberto Bemporad. "Estimation of jump Box–Jenkins models." Automatica 120 (October 2020): 109126. http://dx.doi.org/10.1016/j.automatica.2020.109126.

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Pintelon, R., J. Schoukens, and P. Guillaume. "Box–Jenkins identification revisited—Part III." Automatica 43, no. 5 (May 2007): 868–75. http://dx.doi.org/10.1016/j.automatica.2006.11.007.

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Dissertations / Theses on the topic "BOX-JENKINS"

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Businger, Mark P. "Suitability of Box-Jenkins modeling for Navy repair parts." Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 1996. http://handle.dtic.mil/100.2/ADA319073.

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Biondi, Franco, and Thomas W. Swetnam. "Box-Jenkins Models of Forest Interior Tree-Ring Chronologies." Tree-Ring Society, 1987. http://hdl.handle.net/10150/261796.

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Time domain properties of 23 tree-ring chronologies derived from a large sample of Douglas-fir and ponderosa pine trees growing in closed-canopy forests of Colorado and New Mexico were analyzed using Box-Jenkins models. A variety of statistical criteria were employed during the identification and validation stages for evaluating the performance of different significant models, and the "best" Box-Jenkins model and its immediate "competitor" were reported for each tree-ring chronology. All series were stationary, and only one was approximately a white noise series. Overall, the ARMA(1,1) model was judged the best for 11 series, and the second for 7 of the remaining 12 series. The AR(2) model was considered the best for 6 series, and the second for 4 of the remaining 17 series. No statistical evidence was found for moving average models, nor for models with more than three different parameters. However, both cyclical (or seasonal) models and third-order autoregressive models with a null second-order parameter were chosen for some series. Fitted models explained from 7 to 51% of the variance of the original ring-index series, with an average of about 22 %. All parameter estimates were positive, and they varied within a relatively small range. From a comparison of all employed criteria, Akaike's Information Criterion (AIC) was the one that performed best in identifying Box-Jenkins models for tree-ring chronologies. Possible distinctions were recognized that would separate the selected models according to species and /or standardization option. Among the 12 chronologies from Colorado sites, all derived using the same standardization option, most Douglas-fir series were best fitted by the ARMA(1,1) model, while most ponderosa pine series were best fitted by the AR(2) model, suggesting a difference in the biological persistence of the two species. On the other hand, most of New Mexico chronologies, developed using various standardization options, were best fitted by the ARMA(1,1) model, and no difference was found between Douglas-fir and ponderosa pine series. Also, models fitted to Colorado chronologies explained a lower amount of variance than those for New Mexico chronologies (averages of 17 versus 29% respectively), and cyclical models were mainly selected for New Mexico series. Although periodicities in Douglas-fir series were probably caused by western spruce budworm outbreaks, similar periodic patterns in ponderosa pine series were more difficult to explain because pine trees in the study area had not been defoliated by that insect. Compared to the original tree-ring chronologies, prewhitened series showed similar short-term growth patterns, reduced long-term growth fluctuations, lower standard deviations, and higher mean sensitivities. Also, cross-correlations between chronologies from the same area usually increased after prewhitening. Since the autocorrelation problem is crucial in analyzing the relationships between different time series, and in removing the biological persistence included in tree-ring chronologies, the Box-Jenkins approach should facilitate the analysis of the dynamic relationships between tree growth and environmental variables.
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Gustavsson, André. "Prognossäkerhet : Tillför en heteroskedastisk modell någon säkerhet hos Box och Jenkins prognosmodeller?" Thesis, Umeå University, Department of Statistics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-34825.

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Drevna, Michael J. "An application of Box-Jenkins transfer functions to natural gas demand forecasting." Ohio : Ohio University, 1985. http://www.ohiolink.edu/etd/view.cgi?ohiou1183999594.

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RIBEIRO, LUIZ CLAUDIO. "IDENTIFICATION OF BOX AND JENKINS: A COPARISON BETWEEN FACE AND PADÉ APPROXIMATION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1992. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=9016@1.

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Desde de 1970, quando Box e Jenkins introduziram os modelos ARMA para análise e previsão de séries temporais, muitos estudos foram desenvolvidos buscando encontrar um método mais eficiente de identificação de tais modelos. Tal fato se deu porque o método por Box e Jenkins, baseado na função de auto-correlação parcial (FACP) não são eficientes quando os modelos apresentam componentes auto- regressivas (AR) e médias móveis (MA). Estudos comparativos realizados anteriormente mostraram que dentre os métodos de identificação já desenvolvidos, o que se mostrou mais eficiente foi o baseado na função de auto-correlação extendida (FACE) de TIAO e TSAY (1992) Recentemente, Kuldeep Kumar introduziu na literatura um método de identificação baseado na teoria de aproximação de Padé. O objetivo deste trabalho é comparar o método da FACE com o método baseado na teoria de aproximação de Padé.
Since 1970, when Box and Jenkins first introduced the ARMA models to analysis and predict of time series data, a lot of studies have been developed to find an efficient identification method for such models. This was due the fact that the identification method proposed by Box and Jenkins, based on Auto-correlation Function (ACF) and Partial Auto-correlation Function (PACF), are inefficient when the models have auto regressive - AR- and moving average - MA- components. Comparative studies undertaken, have shown that, among the identification methods already developed, the method based on the Extended Auto-correlation Fuction of Tiao and Tsay (1982) is the most efficient. More recently, however, Kuldeep Kumar has introduced in the literature an identification method based on the theory of Padé aproximation. The objective of this paper is to compare the Extended Auto-correlation Function method with the method based on the Theory of Padé approximation.
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Queirós, Emerson Oliveira de. "Modelo de previsão para receita tributária estadual: aplicação da metodologia Box-Jenkins." Pontifícia Universidade Católica de São Paulo, 2012. https://tede2.pucsp.br/handle/handle/9196.

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The goal of the present work it to use the Box-Jenkins methodology, also known as ARIMA methodology, to build a short-term model of prediction for the state tax (ICMS) revenue. This tax is the most important source of income for the states in Brazil. Therefore, to predict precisely the volume of resources to be collected, other then being a legal requirement, may also be crucial for the financial management of the States. The results achieved indicate that the Box-Jenkins methodology can be a useful tool to forecast the short-term tax revenue from taxes with the characteristics of the ICMS (Value Add Tax)
O presente trabalho objetiva aplicar a metodologia Box-Jenkins, conhecida também como metodologia ARIMA, a fim de construir um modelo de previsão de curto prazo para o imposto estadual (ICMS). Trata-se de um imposto de grande peso relativo nas receitas dos estados no Brasil. Portanto, antecipar precisamente o volume de recursos advindos da principal fonte de receita dos estados no Brasil, além de ser uma imposição legal, pode ser também crucial na gestão financeira dos Estados. Os resultados indicaram que a metodologia Box-Jenkins pode ser uma ferramenta útil se a intenção for construir um modelo de previsão de curto prazo para o imposto com as características do ICMS
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BALTAR, BRUNO DE PAULA. "TEMPORAL ANALYSIS OF COMMODITY COPPER PRICES´S USING THE BOX & JENKINS MODEL." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2009. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=13919@1.

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Essa dissertação aborda o comportamento da série de preços de uma commodity. Busca-se nessa pesquisa aplicar o modelo Box & Jenkins e verificar se este influencia a série de preços da commodity cobre. O estudo inicia-se com um histórico sobre esse mineral, posteriormente resgata-se a evolução dos trabalhos sobre esse tema e descreve-se detalhadamente esse modelo estatístico. Complementarmente ao estudo teórico, foi analisada uma série histórica de retornos de preços da commodity cobre com 19 anos de observações diárias do período entre 1990 e 2008, aplicando-se a metodologia Box & Jenkins. Foram realizados testes para normalidade, estacionaridade e auto-correlação, escolhendose os melhores modelos a serem utilizados. Ao final, conclui-se que os retornos da série de preços são influenciados pelos seus retornos passados, entretanto, baseando-se apenas nessa variável, o seu modelo de previsão a curto prazo tem performance apenas razoável.
This paper studies the behavior of copper prices following the Box & Jenkins model. The dissertation aims to test the validity of this model in explaining the behavior of this commodity. Copper presents one of the most liquid contract among commodities which may increase the information within its price dynamics. This paper is structured as follows: the first section presents a brief historic evolution of copper prices; the second presents relevant previous papers on this matter; the third presents a deep description of the model used and; the fourth, the conclusion. The data set comprises 19 years of daily prices, between 1990 and 2008. Tests for normality, estacionarity and auto-correlation had been carried through, identifying the best models to be used. The paper concludes that past copper price returns partially explain the series future behavior. However, short term forecasting based only on this variable posts just modest performance.
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PINTO, RODRIGO BASTOS. "BEHAVIORAL FINANCE AND BOX AND JENKINS METHODOLOGY: AN APPLICATION ON THE BRAZILIAN MARKET." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2006. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8764@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
Esta dissertação aborda um tema bastante recente e ainda controverso, intitulado Finanças Comportamentais. O estudo se inicia com a apresentação de alguns dos conceitos e estudos já realizados nesta área, onde estão inseridas algumas críticas à hipótese de mercado eficiente e à idéia de caminho aleatório. Estas críticas levam a outros três conceitos, conhecidos como auto-correlação entre os retornos, reversão a média e previsibilidade do retorno de ativos, que são, na verdade, o interesse central do trabalho. Para explorar estes três conceitos será aplicada a metodologia de Box&Jenkins sobre as séries de retornos diários das 50 ações mais líquidas listadas na BOVESPA, sendo que o período analisado vai de 01/01/1994 até 31/12/2005. Ao final, conclui-se que existem evidências de autocorrelação entre os retornos diários das ações, que existe uma possível indicação de que os retornos oscilam em torno de uma média e de que o modelo de previsão baseado em resultados passados tem performance, apenas, razoável.
This dissertation approaches a very recent and controversial issue named Behavioral Finance. So, this work begins presenting some of the concepts and studies carried out in the area, where some criticism of the efficient market hypothesis and the random walk idea is made. This criticism drives to another three concepts: autocorrelation of asset return, mean reversion and predictability of asset return, which, indeed, are the central issues of this work. To explore these three concepts the Box&Jenkins model will be applied on daily return time series of the most 50 liquid stocks listed in the São Paulo Stock Exchange (BOVESPA), between 01/011994 thru 12/31/2005. At the end, the study concludes that exist autocorrelation evidences among daily returns, that there is a possible indication of mean reversion, and that the forecast model based on past results has just a regular performance.
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Thibodeau, Katlyn. "Application de la méthodologie Box-Jenkins aux séries du Ministère de la Santé." Thèse, Université du Québec à Trois-Rivières, 2011. http://depot-e.uqtr.ca/2710/1/030290608.pdf.

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Claudio, Cordeiro Teti Aloisio. "Modelo de previsão da receita tributária : o caso do ICMS no Estado de Pernambuco." Universidade Federal de Pernambuco, 2009. https://repositorio.ufpe.br/handle/123456789/3786.

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Esta dissertação tem como principal objetivo apresentar os modelos de previsão de arrecadação do ICMS, por segmento econômico, para a Secretaria da Fazenda do Estado de Pernambuco, utilizando as técnicas econométricas. Objetiva-se, com essa pesquisa, disponibilizar aos gestores púbicos do Estado mais um modelo de previsão consistente e com certo grau de confiabilidade. Para tanto, utilizou-se da metodologia Box-Jenkins, mais especificamente os modelos: ARIMA - modelo autorregressivo integrado de média móvel, e SARIMA - modelo autorregressivo integrado de média móvel sazonal, e o software RATS (Regression Analyse Time Series). O trabalho apresenta o comportamento da arrecadação de ICMS no Estado e uma revisão da literatura, onde são abordados os principais conceitos teóricos utilizados, bem como uma análise dos resultados obtidos. Conclui-se que o modelo de previsão utilizando séries temporais, em função de sua capacidade preditiva, pode se transformar em um valioso instrumento para auxiliar na elevação da receita tributária no Estado de Pernambuco, dentro da capacidade contributiva de cada contribuinte
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Books on the topic "BOX-JENKINS"

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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. Frankfurt am Main: P. Lang, 1986.

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

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Abou-El-Fetouh, Mohamed Fathi. Forecasting the number of external pilgrims: A Box-Jenkins approach. [Riyadh, Saudi Arabia]: King Saud University, College of Administrative Sciences, Research Center, 1987.

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Bedoya, Manuel Otarola. El ciclo cuantitativo: Una aplicación metodológica no estacionaria para el corto plazo en el caso peruano, período 1989-1992. Lima, Perú: Facultad de Economía, Universidad de Lima, 1993.

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Löderbusch, Bernhard. Modelle zur Aktienkursprognose auf der Basis der Box/Jenkins-Verfahren: Eine empirische Untersuchung. Krefeld: G. Marchal und H.-J. Matzenbacher Wissenschaftsverlag, 1985.

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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. Bergisch Gladbach: J. Eul, 1989.

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Babeshko, Lyudmila, and Irina Orlova. Econometrics and econometric modeling in Excel and R. ru: 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 evaluation, analysis, and practical implementation of Box — Jenkins stationary time series models, as well as multidimensional time series models: vector autoregressive models and vector error correction models. It includes basic econometric models for panel data that have been widely used in recent decades, as well as formal tests for selecting models based on their hierarchical structure. Each section provides examples of evaluating, analyzing, and testing models in the R software environment. Meets the requirements of the Federal state educational standards of higher education of the latest generation. It is addressed to master's students studying in the Field of Economics, the curriculum of which includes the disciplines Econometrics (advanced course)", "Econometric modeling", "Econometric research", and graduate students."
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Suitability of Box-Jenkins Modeling for Navy Repair Parts. Storming Media, 1996.

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Pankratz, Alan. Forecasting with Univariate Box - Jenkins Models: Concepts and Cases. Wiley & Sons, Incorporated, John, 2009.

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Zappe. Forecasting with Univariate Box-Jenkins Models: Co Ncepts and Cases, Second Edition. John Wiley & Sons Inc, 2008.

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Book chapters on the topic "BOX-JENKINS"

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Cipra, Tomas. "Box–Jenkins Methodology." In Time Series in Economics and Finance, 123–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-46347-2_6.

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Tunnicliffe Wilson, Granville, and Peter Armitage. "Box-Jenkins Seasonal Models." In System Identification, Environmental Modelling, and Control System Design, 153–70. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-974-1_8.

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Aljandali, Abdulkader. "The Box-Jenkins Methodology." In Multivariate Methods and Forecasting with IBM® SPSS® Statistics, 59–79. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56481-4_3.

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Boland, John. "Box–Jenkins Time Series Models." In International Encyclopedia of Statistical Science, 178–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_153.

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Mills, Terence C. "Box and Jenkins: Developments Post-1970." In A Very British Affair, 240–87. London: Palgrave Macmillan UK, 2013. http://dx.doi.org/10.1057/9781137291264_8.

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Schlittgen, Rainer. "Box-Jenkins Prognosen mit Fehlerhaften Modellen." In Operations Research Proceedings, 490–97. Berlin, Heidelberg: Springer Berlin Heidelberg, 1985. http://dx.doi.org/10.1007/978-3-642-70457-4_125.

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Asteriou, Dimitrios, and Stephen G. Hall. "ARIMA Models and the Box-Jenkins Methodology." In Applied Econometrics, 275–96. London: Macmillan Education UK, 2016. http://dx.doi.org/10.1057/978-1-137-41547-9_13.

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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, 163–69. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15859-9_23.

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Stahl, Herbert, Mikael Weigelt, and Götz Wiegand. "Box-Jenkins Analysis of Air Pollution Data." In Operations Research Proceedings, 417–24. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/978-3-642-73778-7_107.

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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, 27–47. London: Springer London, 2012. http://dx.doi.org/10.1007/978-0-85729-974-1_2.

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Conference papers on the topic "BOX-JENKINS"

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Darwish, Mohamed, Pepijn Cox, Gianluigi Pillonetto, and Roland Toth. "Bayesian identification of LPV Box-Jenkins models." In 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, 2015. http://dx.doi.org/10.1109/cdc.2015.7402087.

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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 & 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 Dakota. For each type of Box & Jenkins models tested, the model parameters are estimated to determine the corresponding optimal models. For BP network models, different input layer sizes, hidden layer sizes, and learning rates are examined. The evaluation metrics are mean absolute error and root mean squared error. Besides, the persistence model is also employed for purpose of comparison. The results show that in general both best performing Box & Jenkins and BP models can provide better forecasts than the persistence model, while the difference between the Box & Jenkins and BP models is actually insignificant.
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Breschi, Valentina, Dario Piga, and Alberto Bemporad. "Maximum-a-posteriori estimation of jump Box-Jenkins models." In 2019 IEEE 58th Conference on Decision and Control (CDC). IEEE, 2019. http://dx.doi.org/10.1109/cdc40024.2019.9030097.

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Ghomi, S. M. T. Fatemi, and K. Forghani. "Airline passenger forecasting using neural networks and Box-Jenkins." In 2016 12th International Conference on Industrial Engineering (ICIE). IEEE, 2016. http://dx.doi.org/10.1109/induseng.2016.7519342.

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Schoukens, J., Y. Rolain, G. Vandersteen, and R. Pintelon. "User friendly Box-Jenkins identification using nonparametric noise models." In 2011 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC 2011). IEEE, 2011. http://dx.doi.org/10.1109/cdc.2011.6160204.

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6

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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Abstract:
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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Hu, Xinyao, Shicong Meng, Cong Shi, Dingyi Han, and Yong Yu. "Predicting Query Duplication with Box-Jenkins Models and Its Applications." In Seventh IEEE International Conference on Peer-to-Peer Computing (P2P 2007). IEEE, 2007. http://dx.doi.org/10.1109/p2p.2007.21.

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Hu, Xinyao, Shicong Meng, Cong Shi, Dingyi Han, and Yong Yu. "Predicting Query Duplication with Box-Jenkins Models and Its Applications." In Seventh IEEE International Conference on Peer-to-Peer Computing (P2P 2007). IEEE, 2007. http://dx.doi.org/10.1109/p2p.2007.4343467.

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Đukec, Damira. "FORECASTING TOURISM DEMAND IN CROATIA USING BOX AND JENKINS METHODOLOGY." In Tourism in Southern and Eastern Europe: Creating Innovative Tourism Experiences: The Way to Extend the Tourist Season. University of Rijeka, Faculty of Tourism and Hospitality Management, 2019. http://dx.doi.org/10.20867/tosee.05.18.

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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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