Relevant bibliographies by topics / ARMA-modeling

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

Academic literature on the topic 'ARMA-modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "ARMA-modeling"

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Yao, Jing-Wu, and Sueo Sugimoto. "ARMA Modeling and Order Determination." Proceedings of the ISCIE International Symposium on Stochastic Systems Theory and its Applications 1996 (May 5, 1996): 113–19. http://dx.doi.org/10.5687/sss.1996.113.

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Chakraborty, M., and S. Prasad. "Multivariate ARMA modeling by scalar algorithms." IEEE Transactions on Signal Processing 41, no. 4 (1993): 1692–97. http://dx.doi.org/10.1109/78.212746.

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Moses, R., J. Cadzow, and A. Beex. "A recursive procedure for ARMA modeling." IEEE Transactions on Acoustics, Speech, and Signal Processing 33, no. 5 (1985): 1188–96. http://dx.doi.org/10.1109/tassp.1985.1164683.

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Jackson, L. B. "Noncausal ARMA modeling of voiced speech." IEEE Transactions on Acoustics, Speech, and Signal Processing 37, no. 10 (1989): 1606–8. http://dx.doi.org/10.1109/29.35403.

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Mignolet, Marc P., and Pol D. Spanos. "MA to ARMA modeling of wind." Journal of Wind Engineering and Industrial Aerodynamics 36 (January 1990): 429–38. http://dx.doi.org/10.1016/0167-6105(90)90326-8.

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Conte, J. P., K. S. Pister, and S. A. Mahin. "Nonstationary ARMA modeling of seismic motions." Soil Dynamics and Earthquake Engineering 11, no. 7 (1992): 411–26. http://dx.doi.org/10.1016/0267-7261(92)90005-x.

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Wei, Wei, and Hao Ma. "Wavelet-Based ARMA Model Application in Power Network." Applied Mechanics and Materials 121-126 (October 2011): 1509–13. http://dx.doi.org/10.4028/www.scientific.net/amm.121-126.1509.

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The theoretical properties of the ARMA model and the modeling process, then, the Shanghai power network and Shenzhen power network in China were established ARMA model and wavelet-based ARMA model fitting, prediction, and finally, to fit forecast The results were compared. It can be seen, combined with relatively good forecasting effect after wavelet.
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Menasri, A., M. Brahimi, R. Frank, and A. Bali. "ARMA Modeling of Artificial Accelerograms for Algeria." Applied Mechanics and Materials 105-107 (September 2011): 348–55. http://dx.doi.org/10.4028/www.scientific.net/amm.105-107.348.

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The main aim of this study is to examine on the real and simulated earthquakes effects. This paper deals with the use of ARMA models in earthquake engineering. The time-varying auto regressive moving average (ARMA) process is used as a simple yet efficient method for simulating earthquake ground motions. This model is capable of reproducing the nonstationary amplitude as well as the frequency content of the earthquake ground accelerations. The moving time-window technique is applied to synthesize the near field earthquakes, Chlef-1, Chlef-2, Chlef-3 and Attaf 1980 recorded on dense soils in Algeria. This model, is based on a low-order, time-invariant ARMA process excited by Gaussian white noise and amplitude modulated using a simple envelope function to account for the non-stationary characteristics. This simple model gives a reasonable fit to the observed ground motion. It is shown that the selected ARMA (2,1) model and the algorithm used for generating the accelerograms are able to preserve the features of the real earthquake records with different frequency content. In this evaluation, the linear and non linear responses of a given soil layer have been adopted. This study suggests the ability to characterize the earthquake by a minimum number of parameters.
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Shouman, Shouman. "MODELING OF EGG SIGNALS USING ARMA COEFFICIENTS." International Conference on Electrical Engineering 2, no. 2 (1999): 245–52. http://dx.doi.org/10.21608/iceeng.1999.62507.

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Ismaeel, Hasnaa A., and Thafer R. Al-Badrany. "Modeling Some Weather Phenomena by Vector ARMA." IRAQI JOURNAL OF STATISTICAL SCIENCES 15, no. 27 (2018): 59–82. http://dx.doi.org/10.33899/iqjoss.2018.159248.

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Dissertations / Theses on the topic "ARMA-modeling"

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Dal, Santo Paul S. "System identification by ARMA modeling." Thesis, Monterey, California. Naval Postgraduate School, 1988. http://hdl.handle.net/10945/23417.

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System identification concerns the mathematical modeling of a system based upon its input and output. It allows the development of a mathematical description when all that is available is the result of a process or the output of a system and not the process or system itself. The purpose of this thesis is to develop algorithms for modeling systems as autoregressive-moving-average processes using the method of instrumental variables, a modification of the ordinary least-squares technique, and a multichannel method based upon processing the input and output data by separate infinite-response filters. The methods developed are tested by computer simulation using several second and third-order test cases and the results are presented
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Li, Tracy Xiaoping. "ARMA lattice modeling for isolated word speech recognition." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0012/MQ52599.pdf.

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Velasco, Solano Carlos Hernando. "ARMA modeling of signals in the time domain." Thesis, Monterey, California. Naval Postgraduate School, 1992. http://hdl.handle.net/10945/23820.

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Approved for public release; distribution is unlimited<br>This thesis develops an iterative algorithm for the design of ARMA models of signals in the time domain. The algorithm is based on optimization techniques, particularly a gradient technique known as the restricted step method is used. The new algorithm is called the iterative Prony method, and the results obtained using this new method are compared to those obtained using the iterative prefiltering algorithm. The thesis shows that the performance of the iterative Prony method is in most of the cases comparable or superior to that of the iterative prefiltering algorithm.
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Leser, Christoph. "On stationary and nonstationary fatigue load modeling using autoregressive moving average (ARMA) models." Diss., Virginia Tech, 1993. http://hdl.handle.net/10919/29319.

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The concise description of one- and multidimensional stationary and non stationary vehicle loading histories for fatigue analysis using stochastic process theory is presented in this study. The load history is considered to have stationary random and nonstationary mean and variance content. The stationary variations are represented by a class of time series referred to as Autoregressive Moving Average (ARMA) models, while a Fourier series is used to account for the variation of the mean and variance. Due to the use of random phase angles in the Fourier series, an ensemble of mean and variance variations is obtained. The methods of nonparametric statistics are used to determine the success of the modeling of nonstationarity. Justification of the method is obtained through comparison of rainflow cycle distributions and resulting fatigue lives of original and simulated loadings. Due to the relatively small number of Fourier coefficients needed together with the use of ARMA models, a concise description of complex loadings is achieved. The overall frequency content and sequential information of the load history is statistically preserved. An ensemble of load histories can be constructed on-line with minimal computer storage capacity as used in testing equipment. The method can be used in a diversity of fields where a concise representation of random loadings is desired.<br>Ph. D.
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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.<br><p>Updated from "Preprint" to "Article" QC 20130627</p>
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Karlsson, Erlendur. "Least squares arma modeling of linear time-varying systems : lattice filter structures and fast RLS algorithms." Diss., Georgia Institute of Technology, 1987. http://hdl.handle.net/1853/15936.

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Huang, Xiaoyan. "Predicting Short-Term Exchange Rates with a Hybrid PPP/UIP Model." Scholarship @ Claremont, 2013. http://scholarship.claremont.edu/scripps_theses/236.

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This study creates a model to predict short-term exchange rates as a combination of the relative purchasing power parity model (Grossman and Simpson 2011) and the interest power parity model. I then use the statistical techniques ARMA and GARCH to account for the variance of the terms. Previous works considered the effects of these models individually, but mine consider them in unison. I consider both in-sample and out-of-sample tests. I use data on five major exchange rates (JPY/USD, CAD/USD, CHF/USD, GBP/USD, and AUD/USD) sampled at a monthly frequency from 1989-2013. My model statistically significantly predicts these exchange rates over the January 2012 to January 2013 period.
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Dahlen, Anders. "Identification of stochastic systems : Subspace methods and covariance extension." Doctoral thesis, KTH, Mathematics, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-3178.

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Wenzel, Anne. "Komponentenzerlegung des Regelleistungsbedarfs mit Methoden der Zeitreihenanalyse." Master's thesis, Universitätsbibliothek Chemnitz, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:ch1-qucosa-66420.

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Im Rahmen der Arbeit wurden die minutengenauen Daten des Regelleistungsbedarfs (Summe aus Sekundärregelleistung und Minutenreserve) der Monate April bis Dezember des Jahres 2009 einer Regelzone einer Zeitreihenanalyse unterzogen und in Komponenten gemäß dem klassischen Komponentenmodell zerlegt. Diese sind die Trendkomponente, ermittelt durch einen gleitenden Durchschnitt mit der Länge einer Stunde, weiterhin zwei periodische Komponenten mit der Periodenlänge einer Stunde sowie der Periodenlänge eines Tages und die Restkomponente, welche mit einem ARIMA(2,1,5)-Prozess modelliert wurde. In der Zukunft sollte das erstellte Modell des Regelleistungsbedarfs durch Hinzunahme einer jahreszeitlichen Komponente noch verbessert werden. Dies war im Rahmen der Arbeit nicht möglich, da keine Daten über einen Zeitraum von mehreren Jahren vorhanden waren. Zusätzlich kann geprüft werden, inwiefern mit dem Komponentenmodell Prognosen durchführbar sind. Dafür sollte die Trendkomponente anders gewählt werden, da sich der hier gewählte Weg zu sehr an den Daten orientiert. Der zweite Teil der Aufgabenstellung dieser Arbeit bestand im Identifizieren inhaltlicher Komponenten, also möglicher Zusammenhänge zwischen dem Regelleistungsbedarf und verschiedenen denkbaren Ursachen. Als potentielle Ursachen wurden der Lastverlauf sowie die Windenergieeinspeisung untersucht. Zwischen der Zeitreihe des Lastverlaufs und der des Regelleistungsbedarfs bestand eine leichte positive Korrelation, zwischen der Zeitreihe der Windenergieeinspeisung und der des Regelleistungsbedarfs eine geringe negative Korrelation.
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Malmgren, Erik, and Annie Zhang. "Risk Modeling of Sustainable Mutual Funds Using GARCH Time Series." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273578.

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The demand for sustainable investments has seen an increase in recent years. There is considerable literature covering backtesting of the performance and risk of socially responsible investments (SRI) compared to conventional investments. However, literature that models and examines the risk characteristics of SRI compared to conventional investments is limited. This thesis seeks to model and compare the risk of mutual funds scoring in the top 10% in terms of sustainability, based on Morningstar Portfolio Sustainability Score, to those scoring in the bottom 10%. We create one portfolio consisting of the top 10% funds and one portfolio consisting of the bottom 10%, for European and global mutual funds separately, thus in total creating 4 portfolios. The analysis is based on data of the funds' returns and Morningstar Portfolio Sustainability Scores during December 2015 to August 2019. Investigating several GARCH models, we find an ARMA-GARCH model with skewed Student's t-distribution as innovation distribution to give the best fit to the daily log-returns of each portfolio. Based on the fitted ARMA-GARCH models with skewed Student's t-distribution, we use a parametric bootstrap method to compute 95% confidence intervals for the difference in long-run volatility and value at risk (VaR) between the portfolios with high and low Morningstar Portfolio Sustainability Scores. This is performed on the portfolios of European and global funds separately. We conclude that, for global and European funds respectively, no significant difference in terms of long-run volatility and VaR is found between the funds in each of the 10% ends of the Morningstar Portfolio Sustainability Score.<br>Efterfrågan av hållbara investeringar har ökat kraftigt de senaste åren. Det finns många studier som genomför backtesting av hållbara investeringars avkastning och risk jämfört med konventionella investeringar. Färre studier har däremot gjorts för att modellera och jämföra investeringarnas riskegenskaper. Denna uppsats syftar till att modellera risken av hållbara investeringar genom att jämföra de 10% fonder med högst Morningstar Portfolio Sustainability Score mot de 10% fonder med lägst score. Jämförelsen görs separat för globala fonder och europeiska fonder, vilket resulterar i totalt 4 portföljer. Analysen baseras på data på fondernas avkasting och Morningstar Portfolio Sustainability Score under tidsperioden december 2015 till augusti 2019. Genom att undersöka flera olika GARCH-modeller, kommer vi fram till att en ARMA-GARCH-modell med skev t-fördelning bäst beskriver den dagliga logaritmerade avkastningen för varje portfölj. Baserat på de anpassade ARMA-GARCH-modellerna, används en "parametric bootstrap"-metod för att beräkna 95%-iga konfidensintervall för skillnaden i långsiktig volatilitet och value at risk (VaR) mellan portföljerna med högt och lågt Morningstar Portfolio Sustainability Score. Detta görs separat för de europeiska och globala fonderna. Vår slutsats är att det, för globala och europeiska fonder, inte råder en signifikant skillnad i långsiktig volatilitet eller VaR mellan fonder med högt och lågt Morningstar Portfolio Sustainability Score.
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Books on the topic "ARMA-modeling"

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Santo, Paul S. Dal. System identification by ARMA modeling. Naval Postgraduate School, 1988.

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Solano, Carlos Hernando Velasco. ARMA modeling of signals in the time domain. Naval Postgraduate School, 1992.

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Fargues, Monique P. TLS-based prefiltering technique for time-domain ARMA modeling. Naval Postgraduate School, 1994.

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Therrien, Charles W. An iterative extension of Prony's method for ARMA signal modeling. Naval Postgraduate School, 1993.

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Kayahan, Gurhan. ARMA modeling. 1988.

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Book chapters on the topic "ARMA-modeling"

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Brockwell, Peter J., and Richard A. Davis. "Modeling and Forecasting with ARMA Processes." In Introduction to Time Series and Forecasting. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29854-2_5.

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Huang, Changquan, and Alla Petukhina. "ARMA and ARIMA Modeling and Forecasting." In Applied Time Series Analysis and Forecasting with Python. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13584-2_4.

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Hoshiya, M., and Y. Saito. "Prediction Control of Structures by ARMA Modeling." In Probabilistic Structural Mechanics: Advances in Structural Reliability Methods. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/978-3-642-85092-9_16.

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Lindquist, Anders, and Giorgio Picci. "Modeling of Stationary Periodic Time Series by ARMA Representations." In Optimization and Its Applications in Control and Data Sciences. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-42056-1_9.

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Wu, Jingyi. "SWT-ARMA Modeling of Shenzhen A-Share Highest Composite Stock Price Index." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31967-0_14.

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Beligiannis, Grigorios, Spiridon Likothanassis, and Lambros Skarlas. "Evolutionary Multi-Model Estimators for ARMA System Modeling and Time Series Prediction." In Artificial Neural Nets Problem Solving Methods. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-44869-1_52.

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Tong, Weimin, and Yijun Li. "Wavelet Method Combining BP Networks and Time Series ARMA Modeling for Data Mining Forecasting." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11539117_21.

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Chen, Xiude, Xiaolin Jia, Yongxing Zhu, Na Cheng, Shengyang Gao, and Meiqian Guan. "The Research on Time Series Modeling of ARMA and Medium/Long-Term Forecasting Method Using Global Ionospheric Harmonic Coefficient." In China Satellite Navigation Conference (CSNC) 2017 Proceedings: Volume I. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-4588-2_48.

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Tómasson, Helgi. "Implementation of Multivariate Continuous-Time ARMA Models." In Continuous Time Modeling in the Behavioral and Related Sciences. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77219-6_15.

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Bhansali, R. J. "Recursive Order Selection for an Arma Process." In Proceedings of the First US/Japan Conference on the Frontiers of Statistical Modeling: An Informational Approach. Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-011-0866-9_8.

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Conference papers on the topic "ARMA-modeling"

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Luo, Huageng, Liping Wang, Don Beeson, and Gene Wiggs. "Pseudo-ARMA Model for Meta-Modeling Extrapolation." In ASME Turbo Expo 2007: Power for Land, Sea, and Air. ASMEDC, 2007. http://dx.doi.org/10.1115/gt2007-27208.

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In spite of exponential growth in computing power, the enormous computational cost of complex and large-scale engineering design problems make it impractical to rely exclusively on original high fidelity simulation codes. Therefore, there has been an increasing interest in the use of fast executing meta-models to alleviate the computational cost required by slow and expensive simulation models — especially for optimization and probabilistic design. However, many state-of-the-art meta-modeling techniques, such as Radial Basis Function (RBF), Gaussian Process (GP), and Kriging can only make good predictions in the case of interpolation. Their ability for extrapolation is not impressive since the models are mathematically constructed for interpolations. Although Multivariate Adaptive Regression Splines (MARS) and Artificial Neural Network (ANN) have been tried for extrapolation problems (forecasting), the results do not always meet accuracy requirements. The autoregressive moving-average (ARMA) model is a popular time series modeling and forecasting tool. It has been widely used in many engineering applications in which all the inputs and outputs are time dependent. Many researchers have tried to extend the time series ARMA modeling technique into so-called spatial ARMA modeling or time-space ARMA modeling. However, the time-space ARMA modeling requires extensive computation in grid data generation as well as in model building, particularly for high dimensional problems. In this paper, a pseudo-ARMA approach is proposed to strengthen meta-modeling extrapolation capability. Each input is randomly sampled at a given mean value and distribution range to form a pseudo-time series. The output variables are evaluated based on input variables, which formulate output variable pseudo time series. The pseudo-ARMA model is built based on the pseudo input and output time series. Using the constructed pseudo-ARMA model, and new input variables generated with extended distribution parameters, such as distribution means and distribution ranges, the output variables can be evaluated to achieve extrapolations. Several numerical examples are presented to demonstrate the proposed approach. The results are compared with Radial Basis Function (RBF) meta-modeling results for both interpolation and extrapolation.
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Batchelor, Philip, Amedeo Chiribiri, Niloufar Zarinabad Nooralipour, and Zoran Cvetkovic. "ARMA regularization of cardiac perfusion modeling." In 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2010. IEEE, 2010. http://dx.doi.org/10.1109/icassp.2010.5495154.

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Chakraborty, M., and S. Prasad. "Multivariate ARMA modeling by scalar algorithms." In Fifth ASSP Workshop on Spectrum Estimation and Modeling. IEEE, 1990. http://dx.doi.org/10.1109/spect.1990.205548.

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Lederle, K. B., S. Gotz, and R. Kasper. "Order reduction with partial ARMA-modeling." In 1999 European Control Conference (ECC). IEEE, 1999. http://dx.doi.org/10.23919/ecc.1999.7099732.

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Celenk, Mehmet, and Inad Al-Jarrah. "Multiresolution ARMA modeling of facial color images." In Electronic Imaging 2002, edited by Edward R. Dougherty, Jaakko T. Astola, and Karen O. Egiazarian. SPIE, 2002. http://dx.doi.org/10.1117/12.467968.

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Bourquard, Aurelien, Hagai Kirshner, and Michael Unser. "Resolution-invariant separable ARMA modeling of images." In 2011 18th IEEE International Conference on Image Processing (ICIP 2011). IEEE, 2011. http://dx.doi.org/10.1109/icip.2011.6115822.

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Wang, Qi, Li-xin Wang, and Qiang Shen. "Modeling strategy of high order ARMA model." In 2016 IEEE Chinese Guidance, Navigation and Control Conference (CGNCC). IEEE, 2016. http://dx.doi.org/10.1109/cgncc.2016.7829097.

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Hu, Zhen, and Sankaran Mahadevan. "Time-Dependent Reliability Analysis Using a New Multivariate Stochastic Load Model." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59185.

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A common strategy for the modeling of stochastic loads in time-dependent reliability analysis is to describe the loads as independent Gaussian stochastic processes. This assumption does not hold for many engineering applications. This paper proposes a Vine-autoregressive-moving average (Vine-ARMA) load model for time-dependent reliability analysis, in problems with a vector of correlated non-Gaussian stochastic loads. The marginal stochastic processes are modeled as univariate ARMA models. The correlations between different univariate ARMA models are captured using the Vine-copula. The ARMA model maintains the correlation over time. The Vine-copula represents not only the correlation between different ARMA models, but also the tail dependence of different ARMA models. The developed Vine-ARMA model therefore can flexibly model a vector of high-dimensional correlated non-Gaussian stochastic processes with the consideration of tail dependence. Due to the complicated structure of the Vine-ARMA model, new challenges are introduced in time-dependent reliability analysis. In order to overcome these challenges, the Vine-ARMA model is integrated with a recently developed single-loop Kriging (SILK) surrogate modeling method. A hydrokinetic turbine blade subjected to a vector of correlated river flow loads is used to demonstrate the effectiveness of the proposed method.
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Bizrah, Asad F., and Mohammad M. AlMuhaini. "Load reliability analysis using ARMA wind speed modeling." In 2015 IEEE 8th GCC Conference and Exhibition (GCCCE). IEEE, 2015. http://dx.doi.org/10.1109/ieeegcc.2015.7060086.

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Prasad, S., and S. Joshi. "Exact recursive least squares algorithms for ARMA modeling." In IEEE International Conference on Acoustics, Speech, and Signal Processing. IEEE, 1987. http://dx.doi.org/10.1109/icassp.1987.1169813.

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Reports on the topic "ARMA-modeling"

1

Therrien, Charles W., and Carlos H. Velasco. An Iterative Extension of Prony's Method for ARMA Signal Modeling. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada278841.

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Carriere, R., and R. L. Moses. High Resolution Radar Target Modeling Using ARMA (Autoregressive Moving Average)Models. Defense Technical Information Center, 1989. http://dx.doi.org/10.21236/ada218212.

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