Relevant bibliographies by topics / Identification of an autoregressive model

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Identification of an autoregressive model'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

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Journal articles on the topic "Identification of an autoregressive model"

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Sarichev, Aleksandr, and Bogdan Perviy. "AUTOREGRESSION MODELS OF SPACE OBJECTS MOVEMENT REPRESENTED BY TLE ELEMENTS." System technologies 2, no. 127 (February 24, 2020): 103–16. http://dx.doi.org/10.34185/1562-9945-2-127-2020-08.

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The developed method, which is a modification of the previously developed methods for constructing autoregressive models, is used to simulate the motion of space objects in the time series of their TLE elements. The modeling system has been developed that includes: determining the optimal volume of training samples in modeling time series of TLE elements; determination of the autoregression order for each variable (TLE element); determination of the optimal structure and identification of the parameters of the autoregressive model for each variable; identification of patterns of evolution of the mean square error of autoregressive models in time based on the modeling of time series of TLE elements according to the principle of "moving interval".
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Gilmour, Timothy P., Thyagarajan Subramanian, Constantino Lagoa, and W. Kenneth Jenkins. "Multiscale Autoregressive Identification of Neuroelectrophysiological Systems." Computational and Mathematical Methods in Medicine 2012 (2012): 1–5. http://dx.doi.org/10.1155/2012/580795.

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Electrical signals between connected neural nuclei are difficult to model because of the complexity and high number of paths within the brain. Simple parametric models are therefore often used. A multiscale version of the autoregressive with exogenous input (MS-ARX) model has recently been developed which allows selection of the optimal amount of filtering and decimation depending on the signal-to-noise ratio and degree of predictability. In this paper, we apply the MS-ARX model to cortical electroencephalograms and subthalamic local field potentials simultaneously recorded from anesthetized rodent brains. We demonstrate that the MS-ARX model produces better predictions than traditional ARX modeling. We also adapt the MS-ARX results to show differences in internuclei predictability between normal rats and rats with 6OHDA-induced parkinsonism, indicating that this method may have broad applicability to other neuroelectrophysiological studies.
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Goryainov, A. V., V. B. Goryainov, and W. M. Khing. "Robust Identification of an Exponential Autoregressive Model." Herald of the Bauman Moscow State Technical University. Series Natural Sciences, no. 4 (91) (August 2020): 42–57. http://dx.doi.org/10.18698/1812-3368-2020-4-42-57.

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One of the most common nonlinear time series (random processes with discrete time) models is the exponential autoregressive model. In particular, it describes such nonlinear effects as limit cycles, resonant jumps, and dependence of the oscillation frequency on amplitude. When identifying this model, the problem arises of estimating its parameters --- the coefficients of the corresponding autoregressive equation. The most common methods for estimating the parameters of an exponential model are the least squares method and the least absolute deviation method. Both of these methods have a number of disadvantages, to eliminate which the paper proposes an estimation method based on the robust Huber approach. The obtained estimates occupy an intermediate position between the least squares and least absolute deviation estimates. It is assumed that the stochastic sequence is described by the autoregressive equation of the first order, is stationary and ergodic, and the probability distribution of the innovations process of the model is unknown. Unbiased, consistency and asymptotic normality of the proposed estimate are established by computer simulation. Its asymptotic variance was found, which allows to obtain an explicit expression for the relative efficiency of the proposed estimate with respect to the least squares estimate and the least absolute deviation estimate and to calculate this efficiency for the most widespread probability distributions of the innovations sequence of the equation of the autoregressive model
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Ursu, Eugen, and Kamil Feridun Turkman. "Periodic autoregressive model identification using genetic algorithms." Journal of Time Series Analysis 33, no. 3 (January 19, 2012): 398–405. http://dx.doi.org/10.1111/j.1467-9892.2011.00772.x.

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Andrews, Beth, and Richard A. Davis. "Model identification for infinite variance autoregressive processes." Journal of Econometrics 172, no. 2 (February 2013): 222–34. http://dx.doi.org/10.1016/j.jeconom.2012.08.009.

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Liu, Jikui, Liyan Yin, Chenguang He, Bo Wen, Xi Hong, and Ye Li. "A Multiscale Autoregressive Model-Based Electrocardiogram Identification Method." IEEE Access 6 (2018): 18251–63. http://dx.doi.org/10.1109/access.2018.2820684.

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Ando, Shigeru. "Exact FFT-based identification of autoregressive (AR) model." Journal of the Acoustical Society of America 146, no. 4 (October 2019): 2846. http://dx.doi.org/10.1121/1.5136873.

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Liu, Xuan, and Jianbao Chen. "Variable Selection for the Spatial Autoregressive Model with Autoregressive Disturbances." Mathematics 9, no. 12 (June 21, 2021): 1448. http://dx.doi.org/10.3390/math9121448.

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Along with the rapid development of the geographic information system, high-dimensional spatial heterogeneous data has emerged bringing theoretical and computational challenges to statistical modeling and analysis. As a result, effective dimensionality reduction and spatial effect recognition has become very important. This paper focuses on variable selection in the spatial autoregressive model with autoregressive disturbances (SARAR) which contains a more comprehensive spatial effect. The variable selection procedure is presented by using the so-called penalized quasi-likelihood approach. Under suitable regular conditions, we obtain the rate of convergence and the asymptotic normality of the estimators. The theoretical results ensure that the proposed method can effectively identify spatial effects of dependent variables, find spatial heterogeneity in error terms, reduce the dimension, and estimate unknown parameters simultaneously. Based on step-by-step transformation, a feasible iterative algorithm is developed to realize spatial effect identification, variable selection, and parameter estimation. In the setting of finite samples, Monte Carlo studies and real data analysis demonstrate that the proposed penalized method performs well and is consistent with the theoretical results.
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Xiuli, Du, and Wang Fengquan. "Modal identification based on Gaussian continuous time autoregressive moving average model." Journal of Sound and Vibration 329, no. 20 (September 2010): 4294–312. http://dx.doi.org/10.1016/j.jsv.2010.04.018.

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Wu, Ping, ChunJie Yang, and ZhiHuan Song. "Recursive Subspace Model Identification Based On Vector Autoregressive Modelling." IFAC Proceedings Volumes 41, no. 2 (2008): 8872–77. http://dx.doi.org/10.3182/20080706-5-kr-1001.01499.

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Dissertations / Theses on the topic "Identification of an autoregressive model"

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Akgun, Burcin. "Identification Of Periodic Autoregressive Moving Average Models." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/1083682/index.pdf.

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In this thesis, identification of periodically varying orders of univariate Periodic Autoregressive Moving-Average (PARMA) processes is mainly studied. The identification of the varying orders of PARMA process is carried out by generalizing the well-known Box-Jenkins techniques to a seasonwise manner. The identification of pure periodic moving-average (PMA) and pure periodic autoregressive (PAR) models are considered only. For PARMA model identification, the Periodic Autocorrelation Function (PeACF) and Periodic Partial Autocorrelation Function (PePACF), which play the same role as their ARMA counterparts, are employed. For parameter estimation, which is considered only to refine model identification, the conditional least squares estimation (LSE) method is used which is applicable to PAR models. Estimation becomes very complicated, difficult and may give unsatisfactory results when a moving-average (MA) component exists in the model. On account of overcoming this difficulty, seasons following PMA processes are tried to be modeled as PAR processes with reasonable orders in order to employ LSE. Diagnostic checking, through residuals of the fitted model, is also performed stating its reasons and methods. The last part of the study demonstrates application of identification techniques through analysis of two seasonal hydrologic time series, which consist of average monthly streamflows. For this purpose, computer programs were developed specially for PARMA model identification.
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PEREIRA, ANGELO SERGIO MILFONT. "IDENTIFICATION MECHANISMS OF SPURIOUS DIVISIONS IN THRESHOLD AUTOREGRESSIVE MODELS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2002. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=3191@1.

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O objetivo desta dissertação é propor um mecanismo de testes para a avaliação dos resultados obtidos em uma modelagem TS-TARX.A principal motivação é encontrar uma solução para um problema comum na modelagem TS-TARX : os modelos espúrios que são gerados durante o processo de divisão do espaço das variáveis independentes.O modelo é uma heurística baseada em análise de árvore de regressão, como discutido por Brieman -3, 1984-. O modelo proposto para a análise de séries temporais é chamado TARX - Threshold Autoregressive with eXternal variables-. A idéia central é encontrar limiares que separem regimes que podem ser explicados através de modelos lineares. Este processo é um algoritmo que preserva o método de regressão por mínimos quadrados recursivo -MQR-. Combinando a árvore de decisão com a técnica de regressão -MQR-, o modelo se tornou o TS-TARX -Tree Structured - Threshold AutoRegression with external variables-.Será estendido aqui o trabalho iniciado por Aranha em -1, 2001-. Onde a partir de uma base de dados conhecida, um algoritmo eficiente gera uma árvore de decisão por meio de regras, e as equações de regressão estimadas para cada um dos regimes encontrados. Este procedimento pode gerar alguns modelos espúrios ou por construção,devido a divisão binária da árvore, ou pelo fato de não existir neste momento uma metodologia de comparação dos modelos resultantes.Será proposta uma metodologia através de sucessivos testes de Chow -5, 1960- que identificará modelos espúrios e reduzirá a quantidade de regimes encontrados, e consequentemente de parâmetros a estimar. A complexidade do modelo final gerado é reduzida a partir da identificação de redundâncias, sem perder o poder preditivo dos modelos TS-TARX .O trabalho conclui com exemplos ilustrativos e algumas aplicações em bases de dados sintéticas, e casos reais que auxiliarão o entendimento.
The goal of this dissertation is to propose a test mechanism to evaluate the results obtained from the TS-TARX modeling procedure.The main motivation is to find a solution to a usual problem related to TS-TARX modeling: spurious models are generated in the process of dividing the space state of the independent variables.The model is a heuristics based on regression tree analysis, as discussed by Brieman -3, 1984-. The model used to estimate the parameters of the time series is a TARX -Threshold Autoregressive with eXternal variables-.The main idea is to find thresholds that split the independent variable space into regimes which can be described by a local linear model. In this process, the recursive least square regression model is preserved. From the combination of regression tree analysis and recursive least square regression techniques, the model becomes TS-TARX -Tree Structured - Threshold Autoregression with eXternal variables-.The works initiated by Aranha in -1, 2001- will be extended. In his works, from a given data base, one efficient algorithm generates a decision tree based on splitting rules, and the corresponding regression equations for each one of the regimes found.Spurious models may be generated either from its building procedure, or from the fact that a procedure to compare the resulting models had not been proposed.To fill this gap, a methodology will be proposed. In accordance with the statistical tests proposed by Chow in -5, 196-, a series of consecutive tests will be performed.The Chow tests will provide the tools to identify spurious models and to reduce the number of regimes found. The complexity of the final model, and the number of parameters to estimate are therefore reduced by the identification and elimination of redundancies, without bringing risks to the TS-TARX model predictive power.This work is concluded with illustrative examples and some applications to real data that will help the readers understanding.
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Braun, Robin [Verfasser]. "Three Essays on Identification in Structural Vector Autoregressive Models / Robin Braun." Konstanz : KOPS Universität Konstanz, 2019. http://d-nb.info/1191693473/34.

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Bertsche, Dominik [Verfasser]. "Three Essays on Identification and Dimension Reduction in Vector Autoregressive Models / Dominik Bertsche." Konstanz : KOPS Universität Konstanz, 2020. http://d-nb.info/1209879778/34.

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Avventi, Enrico, Anders Lindquist, and Bo Wahlberg. "ARMA Identification of Graphical Models." KTH, Optimeringslära och systemteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-39065.

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Consider a Gaussian stationary stochastic vector process with the property that designated pairs of components are conditionally independent given the rest of the components. Such processes can be represented on a graph where the components are nodes and the lack of a connecting link between two nodes signifies conditional independence. This leads to a sparsity pattern in the inverse of the matrix-valued spectral density. Such graphical models find applications in speech, bioinformatics, image processing, econometrics and many other fields, where the problem to fit an autoregressive (AR) model to such a process has been considered. In this paper we take this problem one step further, namely to fit an autoregressive moving-average (ARMA) model to the same data. We develop a theoretical framework and an optimization procedure which also spreads further light on previous approaches and results. This procedure is then applied to the identification problem of estimating the ARMA parameters as well as the topology of the graph from statistical data.

Updated from "Preprint" to "Article" QC 20130627

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Yang, Kai. "Essays on Multivariate and Simultaneous Equations Spatial Autoregressive Models." The Ohio State University, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=osu1461277549.

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Bruns, Martin [Verfasser]. "Essays in Empirical Macroeconomics: Identification in Vector Autoregressive Models and Robust Inference in Early Warning Systems / Martin Bruns." Berlin : Freie Universität Berlin, 2019. http://d-nb.info/119064522X/34.

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Ogbonna, Emmanuel. "A multi-parameter empirical model for mesophilic anaerobic digestion." Thesis, University of Hertfordshire, 2017. http://hdl.handle.net/2299/17467.

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Anaerobic digestion, which is the process by which bacteria breakdown organic matter to produce biogas (renewable energy source) and digestate (biofertiliser) in the absence of oxygen, proves to be the ideal concept not only for sustainable energy provision but also for effective organic waste management. However, the production amount of biogas to keep up with the global demand is limited by the underperformance in the system implementing the AD process. This underperformance is due to the difficulty in obtaining and maintaining the optimal operating parameters/states for anaerobic bacteria to thrive with regards to attaining a specific critical population number, which results in maximising the biogas production. This problem continues to exist as a result of insufficient knowledge of the interactions between the operating parameters and bacterial community. In addition, the lack of sufficient knowledge of the composition of bacterial groups that varies with changes in the operating parameters such as temperature, substrate and retention time. Without sufficient knowledge of the overall impact of the physico-environmental operating parameters on anaerobic bacterial growth and composition, significant improvement of biogas production may be difficult to attain. In order to mitigate this problem, this study has presented a nonlinear multi-parameter system modelling of mesophilic AD. It utilised raw data sets generated from laboratory experimentation of the influence of four operating parameters, temperature, pH, mixing speed and pressure on biogas and methane production, signifying that this is a multiple input single output (MISO) system. Due to the nonlinear characteristics of the data, the nonlinear black-box modelling technique is applied. The modelling is performed in MATLAB through System Identification approach. Two nonlinear model structures, autoregressive with exogenous input (NARX) and Hammerstein-Wiener (NLHW) with different nonlinearity estimators and model orders are chosen by trial and error and utilised to estimate the models. The performance of the models is determined by comparing the simulated outputs of the estimated models and the output in the validation data. The approach is used to validate the estimated models by checking how well the simulated output of the models fits the measured output. The best models for biogas and methane production are chosen by comparing the outputs of the best NARX and NLHW models (each for biogas and methane production), and the validation data, as well as utilising the Akaike information criterion to measure the quality of each model relative to each of the other models. The NLHW models mhw2 and mhws2 are chosen for biogas and methane production, respectively. The identified NLHW models mhw2 and mhws2 represent the behaviour of the production of biogas and methane, respectively, from mesophilic AD. Among all the candidate models studied, the nonlinear models provide a superior reproduction of the experimental data over the whole analysed period. Furthermore, the models constructed in this study cannot be used for scale-up purpose because they are not able to satisfy the rules and criteria for applying dimensional analysis to scale-up.
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Uhrin, Gábor B. [Verfasser], Martin [Akademischer Betreuer] Wagner, and Walter [Gutachter] Krämer. "In search of Q: results on identification in structural vector autoregressive models / Gábor B. Uhrin ; Gutachter: Walter Krämer ; Betreuer: Martin Wagner." Dortmund : Universitätsbibliothek Dortmund, 2017. http://d-nb.info/1138115134/34.

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Úriz-Jáuregui, Fermín. "Mise en place d'une méthodologie pour l'identification de modèles d'extrapolation de température : application aux équipements de nacelles de turboréacteurs." Thesis, Université de Lorraine, 2012. http://www.theses.fr/2012LORR0381/document.

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Airbus réalise pour chaque avion et pour chaque équipement de nombreux essais, au sol ou en vol et doit garantir qu'en tout point de vol possible, la température de chacun des équipements reste inférieure à la température limite correspondante. Pour pouvoir valider la température de chaque équipement dans l'enveloppe de vol, il faudrait disposer d'essais réalisés aux frontières. Or, tous les essais en vol sont confrontés aux contraintes climatiques et opérationnelles qui ne permettent pas d'explorer tout le domaine. C'est pourquoi Airbus a besoin d'élaborer des méthodes d'extrapolation de température, de manière à prédire le comportement thermique des matériaux et des équipements dans les pires conditions. Les techniques proposées sont basées sur la théorie de l'identification de systèmes qui consiste à déterminer des modèles de comportement d'un point de vue heuristique à partir de mesures et considérations physiques. Plus précisément, le présent document valide les modèles ARX comme un outil pour l'identification de la température du système. Les modèles et les techniques sont étudiés, tout d'abord, d'un point de vue de la simulation numérique et après, confrontés face à des tests représentatifs au laboratoire. Les techniques proposées permettent prédire la température des composants avion pour des conditions différentes
Airbus must ensure that for all flight conditions that a given aircraft could face, the temperature of each powerplant system must be less than the corresponding critical temperature. In order to validate the temperature of each device in the flight envelope, tests at the border should be done. Airbus produces for each aircraft component many trials, either in flight or ground. However, all flight tests are faced with climatic and operational constraints which do not permit exploring the whole area. That's why Airbus needs to develop methods of extrapolation of temperature in order to predict the thermal behavior of materials and equipments in the worst conditions. The proposed techniques are based on the system identification theory which consists on heuristically determining an analytical model using physical insights and measurements. More precisely, this paper validates ARX models as a tool for the identification of the system's temperature. The models and techniques are studied, first, from a numerical simulation point of view and second, based on laboratory representative tests. The proposed techniques allow predicting the temperature of aircraft components at different conditions
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Books on the topic "Identification of an autoregressive model"

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Choi, ByoungSeon. ARMA model identification. New York: Springer-Verlag, 1992.

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Brüggemann, Ralf. Model Reduction Methods for Vector Autoregressive Processes. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17029-4.

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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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Engle, R. F. Forecasting transaction rates: The autoregressive conditional duration model. Cambridge, MA: National Bureau of Economic Research, 1994.

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Hellendoorn, Hans, and Dimiter Driankov, eds. Fuzzy Model Identification. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-642-60767-7.

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Choi, ByoungSeon. ARMA Model Identification. New York, NY: Springer US, 1992. http://dx.doi.org/10.1007/978-1-4613-9745-8.

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Goodwin, Graham, ed. Model Identification and Adaptive Control. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0711-8.

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Abonyi, János. Fuzzy Model Identification for Control. Boston, MA: Birkhäuser Boston, 2003. http://dx.doi.org/10.1007/978-1-4612-0027-7.

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Mocan, H. Naci. Business cycles and fertility dynamics in the U.S.: A vector-autoregressive model. Cambridge, MA (1050 Massachusetts Avenue, Cambridge, MA 02138): National Bureau of Economic Research, 1989.

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Billings, S. A. Model identification and assessment based on model predicted output. Sheffield: University of Sheffield, Dept. of Automatic Control and Systems Engineering, 1998.

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Book chapters on the topic "Identification of an autoregressive model"

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Xia, Qiang, and Heung Wong. "Identification of Threshold Autoregressive Moving Average Models." In Advances in Time Series Methods and Applications, 195–214. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-6568-7_9.

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Hoshiya, Masaru, and Osamu Maruyama. "Identification of Autoregressive Process Model by the Extended Kalman Filter." In Lecture Notes in Engineering, 173–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-84362-4_16.

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Martinez-Vargas, Juan David, Jose David Lopez, Felipe Rendón-Castrillón, Gregor Strobbe, Pieter van Mierlo, German Castellanos-Dominguez, and Diana Ovalle-Martínez. "Identification of Nonstationary Brain Networks Using Time-Variant Autoregressive Models." In Natural and Artificial Computation for Biomedicine and Neuroscience, 426–34. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59740-9_42.

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Kraemer, P., and Claus Peter Fritzen. "Sensor Fault Identification Using Autoregressive Models and the Mutual Information Concept." In Damage Assessment of Structures VII, 387–92. Stafa: Trans Tech Publications Ltd., 2007. http://dx.doi.org/10.4028/0-87849-444-8.387.

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Bovbel, Eugeny E., Igor E. Kheidorov, and Michael E. Kotlyar. "Speaker Identification Using Autoregressive Hidden Markov Models and Adaptive Vector Quantisation." In Text, Speech and Dialogue, 207–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-45323-7_35.

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Horváth, Lajos, and Piotr Kokoszka. "Functional autoregressive model." In Springer Series in Statistics, 235–52. New York, NY: Springer New York, 2012. http://dx.doi.org/10.1007/978-1-4614-3655-3_13.

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Shekhar, Shashi, and Hui Xiong. "Simultaneous Autoregressive Model (SAR)." In Encyclopedia of GIS, 1056. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-35973-1_1217.

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Nikolov, Ventsislav. "Autoregressive Model Order Determination." In Advances in Intelligent Systems and Computing, 577–87. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01057-7_45.

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Roy, Radhika Ranjan. "Autoregressive Group Mobility Model." In Handbook of Mobile Ad Hoc Networks for Mobility Models, 791–806. Boston, MA: Springer US, 2010. http://dx.doi.org/10.1007/978-1-4419-6050-4_32.

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Priestley, M. B. "Autoregressive Model Fitting and Windows." In Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach, 63–78. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0866-9_5.

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Conference papers on the topic "Identification of an autoregressive model"

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Kim, Sung-Ho, and Namgil Lee. "A Bayes Shrinkage Estimation Method for Vector Autoregressive Models." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2012. http://dx.doi.org/10.2316/p.2012.769-065.

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Choi, Jaewon, Mohsen Nakhaeinejad, and Michael D. Bryant. "System Identification of Small Loudspeakers Using ARMA Model." In ASME 2010 Dynamic Systems and Control Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/dscc2010-4221.

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This study illustrates a data driven system identification method for loudspeaker model estimation using the knowledge of the underlying physics of loudspeakers. In this study, diaphragm displacement is analyzed to estimate the model structure and parameters based on impulse response equivalent sampling and autoregressive moving average model. The estimated loudspeaker models are compared in the frequency response function plot. It is shown that the autoregressive moving average (ARMA) based loudspeaker models are comparable to the model estimated by the conventional method based on electrical impedance. Also ARMA modeling strategies with and without knowledge of the physics-based model are compared. Some issues related to ARMA modeling are addressed.
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Angarita, John, Daniel Doyle, Gustavo Gargioni, and Jonathan Black. "Input Excitation Analysis for Black-Box Quadrotor Model System Identification." In ASME 2020 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/dscc2020-3159.

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Abstract System identification provides a process to develop different dynamic models of varying structures based on user-defined requirements. For a quadrotor, system identification has been primarily in the field of off-white and grey-box models, but black-box models have the advantage of incorporating nonlinear aero-dynamic effects while also maintaining performance. For the identification, both a chirp and Hebert-Mackin parameter identification method waveform are used as inputs to maximize excitation while minimizing nonlinear responses. The quadrotor structure is defined by the an autoregressive with exogenous input (ARX) model, an autoregressive-moving-average (ARMAX) model, and a Box-Jenkins (BJ) models and then identified with the prediction error method. The black-box method shows that it maintains identification performance while improving upon the flexibility of different cases and ease of implementation.
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Zhao, Sijia, Ke Liu, and Xin Deng. "EEG Identification Based on Brain Functional Network and Autoregressive Model." In 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 2020. http://dx.doi.org/10.1109/ddcls49620.2020.9275241.

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Kim, Sung-Ho, and Yongtae Kim. "Structure Learning of Multivariate Autoregressive Models based on Marginal Models." In Artificial Intelligence and Applications / Modelling, Identification, and Control. Calgary,AB,Canada: ACTAPRESS, 2011. http://dx.doi.org/10.2316/p.2011.718-067.

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Soma, Hitoshi, and Kaneo Hiramatsu. "Identification of Vehicle Dynamics Under Lateral Wind Disturbance Using Autoregressive Model." In International Pacific Conference On Automotive Engineering. 400 Commonwealth Drive, Warrendale, PA, United States: SAE International, 1993. http://dx.doi.org/10.4271/931894.

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Veloz, A., R. Salas, H. Allende-Cid, and H. Allende. "SIFAR: Self-Identification of Lags of an Autoregressive TSK-based Model." In 2012 IEEE 42nd International Symposium on Multiple-Valued Logic (ISMVL). IEEE, 2012. http://dx.doi.org/10.1109/ismvl.2012.42.

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Liu, Lilan, Hongzhao Liu, Ziying Wu, Daning Yuan, and Pengfei Li. "Modal Parameter Identification of Time-Varying Systems Using the Time-Varying Multivariate Autoregressive Model." In ASME 2005 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2005. http://dx.doi.org/10.1115/detc2005-84118.

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A new time-varying multivariate autoregressive (TVMAR) model method for modal parameter identification of linear time-varying (TV) systems with multi-output is introduced. Besides, a modified recursive least square method based on the traditional one is presented to determine the coefficient matrices of the TVMAR model. In the proposed method, multi-dimensional nonstationary response signals of the vibrating system can be processed simultaneously. Not only the TV modal frequency and damping ratio of the system, but also the changing behavior of the mode shape in the course of vibration are identified by the proposed procedure. Numerical simulations, in which a three-degree-of-freedom system with TV stiffness is respectively subjected to impulse excitation and white noise excitation, are presented. The validity and accuracy of the method are demonstrated by the good simulation results.
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Tiedemann, Kenneth H. "Modelling Residential and Commercial Demand for Electricity Using Autoregressive Distributed Lag Models." In Modelling, Identification and Control / 827: Computational Intelligence. Calgary,AB,Canada: ACTAPRESS, 2015. http://dx.doi.org/10.2316/p.2015.826-013.

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Firat, Umut, Seref Naci Engin, Murat Saraclar, and Aysin Baytan Ertuzun. "Wind Speed Forecasting Based on Second Order Blind Identification and Autoregressive Model." In 2010 International Conference on Machine Learning and Applications (ICMLA). IEEE, 2010. http://dx.doi.org/10.1109/icmla.2010.106.

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Reports on the topic "Identification of an autoregressive model"

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Engle, Robert, and Jeffrey Russell. Forecasting Transaction Rates: The Autoregressive Conditional Duration Model. Cambridge, MA: National Bureau of Economic Research, December 1994. http://dx.doi.org/10.3386/w4966.

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Rosser, J. Barkley, and Richard G. Sheehan. A Vector Autoregressive Model of Saudi Arabian Inflation. Federal Reserve Bank of St. Louis, 1985. http://dx.doi.org/10.20955/wp.1985.011.

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Wahba, Grace. Multivariate Model Building and Model Identification. Fort Belvoir, VA: Defense Technical Information Center, April 1990. http://dx.doi.org/10.21236/ada221619.

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Ahmed, Ehsan, J. Barkley Rosser, and Richard G. Sheehan. A Model of Global Aggregate Supply and Demand Using Vector Autoregressive Techniques. Federal Reserve Bank of St. Louis, 1986. http://dx.doi.org/10.20955/wp.1986.004.

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Mocan, Naci. Business Cycles and Fertility Dynamics in the U.S.: A Vector-Autoregressive Model. Cambridge, MA: National Bureau of Economic Research, November 1989. http://dx.doi.org/10.3386/w3177.

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Dewald, Lee S., Peter A. Lewis, and Ed McKenzie. A Bivariate First Order Autoregressive Time Series Model in Exponential Variables (BEAR(1)). Fort Belvoir, VA: Defense Technical Information Center, October 1986. http://dx.doi.org/10.21236/ada177055.

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Yamada, Tadashi. The Crime Rate and the Condition of the Labor Market: A Vector Autoregressive Model. Cambridge, MA: National Bureau of Economic Research, December 1985. http://dx.doi.org/10.3386/w1782.

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Cyganski, David, R. F. Vaz, and J. A. Orr. Model-Based 3-D Object Identification. Fort Belvoir, VA: Defense Technical Information Center, March 1998. http://dx.doi.org/10.21236/ada344653.

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Red-Horse, J. R. Structural system identification: Structural dynamics model validation. Office of Scientific and Technical Information (OSTI), April 1997. http://dx.doi.org/10.2172/469145.

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Alberti, Jose. Modeling and Model Identification of Autonomous Underwater Vehicles. Fort Belvoir, VA: Defense Technical Information Center, June 2015. http://dx.doi.org/10.21236/ad1009363.

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