Relevant bibliographies by topics / Vector autoregressive moving average

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
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Academic literature on the topic 'Vector autoregressive moving average'

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

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Journal articles on the topic "Vector autoregressive moving average"

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Fitrianti, H., S. M. Belwawin, M. Riyana, and R. Amin. "Climate modeling using vector moving average autoregressive." IOP Conference Series: Earth and Environmental Science 343 (November 6, 2019): 012201. http://dx.doi.org/10.1088/1755-1315/343/1/012201.

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Iwok, I. A., and E. H. Etuk. "On the Comparative Performance of Pure Vector Autoregressive-Moving Average and Vector Bilinear Autoregressive-Moving Average Time Series Models." Asian Journal of Mathematics & Statistics 2, no. 2 (April 15, 2009): 33–40. http://dx.doi.org/10.3923/ajms.2009.33.40.

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Iwok, I. A., and E. H. Etuk. "On the Comparative Performance of Pure Vector Autoregressive-Moving Average and Vector Bilinear Autoregressive-Moving Average Time Series Models." Asian Journal of Mathematics & Statistics 3, no. 3 (June 15, 2010): 179–86. http://dx.doi.org/10.3923/ajms.2010.179.186.

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Tsay, Ruey S. "Parsimonious Parameterization of Vector Autoregressive Moving Average Models." Journal of Business & Economic Statistics 7, no. 3 (July 1989): 327. http://dx.doi.org/10.2307/1391530.

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Tsay, Ruey S. "Parsimonious Parameterization of Vector Autoregressive Moving Average Models." Journal of Business & Economic Statistics 7, no. 3 (July 1989): 327–41. http://dx.doi.org/10.1080/07350015.1989.10509742.

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Anggraeni, Wiwik, and Leivina Kartika Dewi. "PERAMALAN MENGGUNAKAN METODE VECTOR AUTOREGRESSIVE MOVING AVERAGE (VARMA)." JUTI: Jurnal Ilmiah Teknologi Informasi 7, no. 2 (July 1, 2008): 107. http://dx.doi.org/10.12962/j24068535.v7i2.a180.

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Ben, Marta Garcia, Elena J. Martinez, and Victor J. Yohai. "Robust Estimation in Vector Autoregressive Moving-Average Models." Journal of Time Series Analysis 20, no. 4 (July 1999): 381–99. http://dx.doi.org/10.1111/1467-9892.00144.

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Koreisha, Sergio G., and Tarmo Pukkila. "The specification of vector autoregressive moving average models." Journal of Statistical Computation and Simulation 74, no. 8 (August 2004): 547–65. http://dx.doi.org/10.1080/00949650310001616559.

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Yozgatligil, Ceylan, and William W. S. Wei. "Representation of Multiplicative Seasonal Vector Autoregressive Moving Average Models." American Statistician 63, no. 4 (November 2009): 328–34. http://dx.doi.org/10.1198/tast.2009.08040.

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Jouini, Tarek. "Linear bootstrap methods for vector autoregressive moving-average models." Journal of Statistical Computation and Simulation 85, no. 11 (June 17, 2014): 2214–57. http://dx.doi.org/10.1080/00949655.2014.925898.

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Dissertations / Theses on the topic "Vector autoregressive moving average"

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Dong, Juntao. "Reinforcement Learning for Multiple Time Series: Forex Trading Application." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1613745680121778.

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Akin, Serdar. "Do Riksbanken produce unbiased forecast of the inflation rate? : and can it be improved?" Thesis, Stockholms universitet, Nationalekonomiska institutionen, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-58708.

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The focus of this paper is to evaluate if forecast produced by the Central Bank of Sweden (Riksbanken) for the 12 month change in the consumer price index is unbiased? Results shows that for shorter horizons (h < 12) the mean forecast error is unbiased but for longer horizons its negatively biased when inference is done by Maximum entropy bootstrap technique. Can the unbiasedness be improved by strict ap- pliance to econometric methodology? Forecasting with a linear univariate model (seasonal ARIMA) and a multivariate model Vector Error Correction model (VECM) shows that when controlling for the presence of structural breaks VECM outperforms both prediction produced Riksbanken and ARIMA. However Riksbanken had the best precision in their forecast, estimated as MSFE
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Ziedzor, Reginald. "GENERALIZED AUTOREGRESSIVE MOVING AVERAGE MODELS." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/theses/2198.

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Alj, Abdelkamel. "Contribution to the estimation of VARMA models with time-dependent coefficients." Doctoral thesis, Universite Libre de Bruxelles, 2012. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/209651.

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Dans cette thèse, nous étudions l’estimation de modèles autorégressif-moyenne mobile

vectoriels ou VARMA, `a coefficients dépendant du temps, et avec une matrice de covariance

des innovations dépendant du temps. Ces modèles sont appel´es tdVARMA. Les éléments

des matrices des coefficients et de la matrice de covariance sont des fonctions déterministes

du temps dépendant d’un petit nombre de paramètres. Une première partie de la thèse

est consacrée à l’étude des propriétés asymptotiques de l’estimateur du quasi-maximum

de vraisemblance gaussienne. La convergence presque sûre et la normalité asymptotique

de cet estimateur sont démontrées sous certaine hypothèses vérifiables, dans le cas o`u les

coefficients dépendent du temps t mais pas de la taille des séries n. Avant cela nous considérons les propriétés asymptotiques des estimateurs de modèles non-stationnaires assez

généraux, pour une fonction de pénalité générale. Nous passons ensuite à l’application de

ces théorèmes en considérant que la fonction de pénalité est la fonction de vraisemblance

gaussienne (Chapitre 2). L’étude du comportement asymptotique de l’estimateur lorsque

les coefficients du modèle dépendent du temps t et aussi de n fait l’objet du Chapitre 3.

Dans ce cas, nous utilisons une loi faible des grands nombres et un théorème central limite

pour des tableaux de différences de martingales. Ensuite, nous présentons des conditions

qui assurent la consistance faible et la normalité asymptotique. Les principaux

résultats asymptotiques sont illustrés par des expériences de simulation et des exemples

dans la littérature. La deuxième partie de cette thèse est consacrée à un algorithme qui nous

permet d’évaluer la fonction de vraisemblance exacte d’un processus tdVARMA d’ordre (p, q) gaussien. Notre algorithme est basé sur la factorisation de Cholesky d’une matrice

bande partitionnée. Le point de départ est une généralisation au cas multivarié de Mélard

(1982) pour évaluer la fonction de vraisemblance exacte d’un modèle ARMA(p, q) univarié. Aussi, nous utilisons quelques résultats de Jonasson et Ferrando (2008) ainsi que les programmes Matlab de Jonasson (2008) dans le cadre d’une fonction de vraisemblance

gaussienne de modèles VARMA à coefficients constants. Par ailleurs, nous déduisons que

le nombre d’opérations requis pour l’évaluation de la fonction de vraisemblance en fonction de p, q et n est approximativement le double par rapport à un modèle VARMA à coefficients

constants. L’implémentation de cet algorithme a été testée en comparant ses résultats avec

d’autres programmes et logiciels très connus. L’utilisation des modèles VARMA à coefficients

dépendant du temps apparaît particulièrement adaptée pour la dynamique de quelques

séries financières en mettant en évidence l’existence de la dépendance des paramètres en

fonction du temps.


Doctorat en Sciences
info:eu-repo/semantics/nonPublished

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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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Chong, Ching Yee. "Portmanteau testing for nonstationary autoregressive moving-average models /." View Abstract or Full-Text, 2003. http://library.ust.hk/cgi/db/thesis.pl?MATH%202003%20CHONG.

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Thesis (M. Phil.)--Hong Kong University of Science and Technology, 2003.
Includes bibliographical references (leaves 37-39). Also available in electronic version. Access restricted to campus users.
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Mohammadipour, Maryam. "Intermittent demand forecasting with integer autoregressive moving average models." Thesis, Bucks New University, 2009. http://bucks.collections.crest.ac.uk/9586/.

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This PhD thesis focuses on using time series models for counts in modelling and forecasting a special type of count series called intermittent series. An intermittent series is a series of non-negative integer values with some zero values. Such series occur in many areas including inventory control of spare parts. Various methods have been developed for intermittent demand forecasting with Croston’s method being the most widely used. Some studies focus on finding a model underlying Croston’s method. With none of these studies being successful in demonstrating an underlying model for which Croston’s method is optimal, the focus should now shift towards stationary models for intermittent demand forecasting. This thesis explores the application of a class of models for count data called the Integer Autoregressive Moving Average (INARMA) models. INARMA models have had applications in different areas such as medical science and economics, but this is the first attempt to use such a model-based method to forecast intermittent demand. In this PhD research, we first fill some gaps in the INARMA literature by finding the unconditional variance and the autocorrelation function of the general INARMA(p,q) model. The conditional expected value of the aggregated process over lead time is also obtained to be used as a lead time forecast. The accuracy of h-step-ahead and lead time INARMA forecasts are then compared to those obtained by benchmark methods of Croston, Syntetos-Boylan Approximation (SBA) and Shale-Boylan-Johnston (SBJ). The results of the simulation suggest that in the presence of a high autocorrelation in data, INARMA yields much more accurate one-step ahead forecasts than benchmark methods. The degree of improvement increases for longer data histories. It has been shown that instead of identification of the autoregressive and moving average order of the INARMA model, the most general model among the possible models can be used for forecasting. This is especially useful for short history and high autocorrelation in data. The findings of the thesis have been tested on two real data sets: (i) Royal Air Force (RAF) demand history of 16,000 SKUs and (ii) 3,000 series of intermittent demand from the automotive industry. The results show that for sparse data with long history, there is a substantial improvement in using INARMA over the benchmarks in terms of Mean Square Error (MSE) and Mean Absolute Scaled Error (MASE) for the one-step ahead forecasts. However, for series with short history the improvement is narrower. The improvement is greater for h-step ahead forecasts. The results also confirm the superiority of INARMA over the benchmark methods for lead time forecasts.
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SCHER, Vinícius Teodoro. "Portmanteau testing inference in beta autoregressive moving average models." Universidade Federal de Pernambuco, 2017. https://repositorio.ufpe.br/handle/123456789/26891.

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Submitted by Pedro Barros (pedro.silvabarros@ufpe.br) on 2018-09-21T20:16:54Z No. of bitstreams: 2 license_rdf: 811 bytes, checksum: e39d27027a6cc9cb039ad269a5db8e34 (MD5) DISSERTAÇÃO Vinícius Teodoro Scher.pdf: 902853 bytes, checksum: 8ce4def07864471ce7717ce6d128d086 (MD5)
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CAPES
The class of beta autoregressive moving average (bARMA) models is useful for modeling time series data that assume values in the standard unit interval, such as rates and proportions. This thesis is composed of two main and independent chapters. In the first part, we consider portmanteau testing inference in the class of bARMA models. To that end, we use tests that have been developed for Gaussian models, such as the Ljung and Box, Monti, Dufour and Roy, Kwan and Sim, and Lin and McLeod tests. We also consider bootstrap variants of the Ljung and Box, Monti, Dufour and Roy, and Kwan and Sim tests. Moreover, we propose two new test statistics which, like the Monti statistic, are based on residual partial autocorrelations. Additionally, we present and discuss results from Monte Carlo simulations and an empirical application. The second part of the thesis focuses on the recursive nature of bARMA loglikelihood derivatives under moving average dynamics. We provide closed form expressions for the relevant derivatives by considering errors in the predictor scale.
A classe de modelos beta autorregressivos de médias móveis (bARMA) é útil para modelar dados que assumem valores no intervalo unitário padrão, como taxas e proporções. A presente dissertação tem como tema tal classe de models e é composta por dois capítulos principais e independentes. Na primeira parte, consideramos inferências baseadas em testes portmanteau na classe de modelos bARMA. Para tanto, utilizamos testes que foram desenvolvidos para modelos gaussianos, como os testes de Ljung e Box, Monti, Dufour e Roy, Kwan e Sim, e Lin e McLeod. Também consideramos variantes bootstrap dos testes de Ljung e Box, Monti, Dufour e Roy and Kwan e Sim. Adicionalmente, propomos duas novas estatísticas de testes que, tal qual a estatística de Monti, são baseadas em autocorrelações parciais dos resíduos. Apresentamos e discutimos resultados de simulações de Monte Carlo e uma aplicação empírica. A segunda parte da dissertação aborda a natureza recursiva das derivadas da função de log-verossimilhança bARMA sob dinâmica de médias móveis. Nós fornecemos expressões em forma fechada para as derivadas relevantes considerando erros na escala do preditor.
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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.
Ph. D.
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Hanh, Nguyen T. "Lasso for Autoregressive and Moving Average Coeffients via Residuals of Unobservable Time Series." University of Toledo / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=toledo154471227291601.

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Books on the topic "Vector autoregressive moving average"

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Biekpe, Nicholas. Financial forecasts: Bilinear autoregressive moving average models. [Belfast]: Accountingand Finance Division, School of Finance and Information, Queen's University of Belfast, 1993.

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Burridge, P. Forecasting and signal extraction in autoregressive-moving average models. University of Warwick Department of Economics, 1986.

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Rathmanner, Steven Clifford. Image texture generation using autoregressive integrated moving average (ARIMA) models. 1987.

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Peramalan jangka pendek harga sayuran di daerah konsumen; aplikasi model autoregressive integrated moving average (arima): Laporan penelitian. Bandung: Lembaga Penelitian, Universitas Padjadjaran, 2000.

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McCleary, Richard, David McDowall, and Bradley J. Bartos. ARIMA Algebra. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0002.

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The goal of Chapter 2 is to derive the properties of common processes and, based on these properties, to develop a general scheme for classifying processes. Stationary processes includes white noise, moving average (MA), and autoregressive (AR) processes. MA and AR models can approximate mixed ARMA models. A lag or backshift operator is used to solve ARIMA models for time series observations or random shocks. Covariance functions are derived for each of the common processes.Maximum likelihood estimates are introduced for the purposes of estimating autoregressive and moving average parameters.
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McCleary, Richard, David McDowall, and Bradley J. Bartos. Noise Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0003.

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Chapter 3 introduces the Box-Jenkins AutoRegressive Integrated Moving Average (ARIMA) noise modeling strategy. The strategy begins with a test of the Normality assumption using a Kolomogov-Smirnov (KS) statistic. Non-Normal time series are transformed with a Box-Cox procedure is applied. A tentative ARIMA noise model is then identified from a sample AutoCorrelation function (ACF). If the sample ACF identifies a nonstationary model, the time series is differenced. Integer orders p and q of the underlying autoregressive and moving average structures are then identified from the ACF and partial autocorrelation function (PACF). Parameters of the tentative ARIMA noise model are estimated with maximum likelihood methods. If the estimates lie within the stationary-invertible bounds and are statistically significant, the residuals of the tentative model are diagnosed to determine whether the model’s residuals are not different than white noise. If the tentative model’s residuals satisfy this assumption, the statistically adequate model is accepted. Otherwise, the identification-estimation-diagnosis ARIMA noise model-building strategy continues iteratively until it yields a statistically adequate model. The Box-Jenkins ARIMA noise modeling strategy is illustrated with detailed analyses of twelve time series. The example analyses include non-Normal time series, stationary white noise, autoregressive and moving average time series, nonstationary time series, and seasonal time series. The time series models built in Chapter 3 are re-introduced in later chapters. Chapter 3 concludes with a discussion and demonstration of auxiliary modeling procedures that are not part of the Box-Jenkins strategy. These auxiliary procedures include the use of information criteria to compare models, unit root tests of stationarity, and co-integration.
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Gereziher, Hayelom Yrgaw, and Naser Yenus Nuru. Structural estimates of the South African sacrifice ratio. 12th ed. UNU-WIDER, 2021. http://dx.doi.org/10.35188/unu-wider/2021/946-4.

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This paper estimates the output cost of fighting inflation—the sacrifice ratio—for the South African economy using quarterly data spanning the period 1998Q1–2019Q3. To compute the sacrifice ratio, the structural vector autoregressive model developed by Cecchetti and Rich (2001) based on Cecchetti (1994) is employed. Our findings show us a small sacrifice ratio, which lies within the range 0.00002–0.231 per cent with an average of 0.031 per cent, indicating a low level of output to be sacrificed while fighting inflation. Hence, the reserve bank is recommended to sustain an inflation rate within the target range and reap the benefits of a predictable and stable price path, as restrictive monetary policy has only a transitory effect on real variables like output.
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McCleary, Richard, David McDowall, and Bradley Bartos. Design and Analysis of Time Series Experiments. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.001.0001.

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Design and Analysis of Time Series Experiments develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioral, and biomedical time series illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. The classic Box-Jenkins-Tiao model-building strategy is supplemented with recent auxiliary tests for transformation, differencing, and model selection. The validity of causal inferences is approached from two complementary directions. The four-validity system of Cook and Campbell relies on ruling out discrete threats to statistical conclusion, internal, construct, and external validity. The Rubin system causal model relies on the identification of counterfactual time series. The two approaches to causal validity are shown to be complementary and are illustrated with a construction of a synthetic control time series. Example analyses make optimal use of graphical illustrations. Mathematical methods used in the example analyses are explicated in technical appendices, including expectation algebra, sequences and series, maximum likelihood, Box-Cox transformation analyses and probability.
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McCleary, Richard, David McDowall, and Bradley J. Bartos. Intervention Modeling. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0005.

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The general AutoRegressive Integrated Moving Average (ARIMA) model can be written as the sum of noise and exogenous components. If an exogenous impact is trivially small, the noise component can be identified with the conventional modeling strategy. If the impact is nontrivial or unknown, the sample AutoCorrelation Function (ACF) will be distorted in unknown ways. Although this problem can be solved most simply when the outcome of interest time series is long and well-behaved, these time series are unfortunately uncommon. The preferred alternative requires that the structure of the intervention is known, allowing the noise function to be identified from the residualized time series. Although few substantive theories specify the “true” structure of the intervention, most specify the dichotomous onset and duration of an impact. Chapter 5 describes this strategy for building an ARIMA intervention model and demonstrates its application to example interventions with abrupt and permanent, gradually accruing, gradually decaying, and complex impacts.
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McDowall, David, Richard McCleary, and Bradley J. Bartos. Interrupted Time Series Analysis. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780190943943.001.0001.

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Interrupted Time Series Analysis develops a comprehensive set of models and methods for drawing causal inferences from time series. Example analyses of social, behavioural, and biomedical time series illustrate a general strategy for building AutoRegressive Integrated Moving Average (ARIMA) impact models. The classic Box-Jenkins-Tiao model-building strategy is supplemented with recent auxiliary tests for transformation, differencing and model selection. New developments, including Bayesian hypothesis testing and synthetic control group designs are described and their prospects for widespread adoption are discussed. Example analyses make optimal use of graphical illustrations. Mathematical methods used in the example analyses are explicated assuming only exposure to an introductory statistics course. Design and Analysis of Time Series Experiments (DATSE) and other appropriate authorities are cited for formal proofs. Forty completed example analyses are used to demonstrate the implications of model properties. The example analyses are suitable for use as problem sets for classrooms, workshops, and short-courses.
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Book chapters on the topic "Vector autoregressive moving average"

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Lütkepohl, Helmut. "Vector Autoregressive Moving Average Processes." In Introduction to Multiple Time Series Analysis, 217–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-61695-2_6.

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Lütkepohl, Helmut. "Vector Autoregressive Moving Average Processes." In New Introduction to Multiple Time Series Analysis, 419–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-27752-1_11.

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Lütkepohl, Helmut. "Vector Autoregressive Moving Average Processes." In Introduction to Multiple Time Series Analysis, 217–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-662-02691-5_6.

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Hosoya, Yuzo, Kosuke Oya, Taro Takimoto, and Ryo Kinoshita. "Inference Based on the Vector Autoregressive and Moving Average Model." In Characterizing Interdependencies of Multiple Time Series, 65–102. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-6436-4_4.

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Neusser, Klaus. "Stationary Time Series Models: Vector Autoregressive Moving-Average Processes (VARMA Processes)." In Springer Texts in Business and Economics, 215–24. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32862-1_12.

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Neusser, Klaus. "Autoregressive Moving-Average Models." In Springer Texts in Business and Economics, 25–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32862-1_2.

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Jones, Richard H. "Autoregressive Moving Average Errors." In Longitudinal Data with Serial Correlation, 120–38. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4489-4_6.

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Tiao, George C. "Univariate Autoregressive Moving-Average Models." In Wiley Series in Probability and Statistics, 53–85. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2011. http://dx.doi.org/10.1002/9781118032978.ch3.

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Hassler, Uwe. "Autoregressive Moving Average Processes (ARMA)." In Stochastic Processes and Calculus, 45–75. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-23428-1_3.

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Nerlove, Marc. "Autoregressive and Moving-Average Time-Series Processes." In The New Palgrave Dictionary of Economics, 608–15. London: Palgrave Macmillan UK, 2018. http://dx.doi.org/10.1057/978-1-349-95189-5_623.

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Conference papers on the topic "Vector autoregressive moving average"

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Koulocheris, Dimitris V., and Vasilis K. Dertimanis. "Parametric Identification of Vehicle’s Vertical Dynamics Using Vector Autoregressive Moving Average Models." In ASME 8th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2006. http://dx.doi.org/10.1115/esda2006-95515.

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The assessment of vertical dynamics in modern ground vehicles is a difficult task with crucial importance, as it appears to be possessed by a number of conflicting objectives, such as ride comfort and stability. Thus, the effective use of possible control units is depended by the successful description of the vertical performance. The aim of this study is to provide a closed description of vehicles’ vertical dynamics using VARMA models, which are estimated by means of a novel, hybrid optimization algorithm and a corresponding estimation procedure. The hybrid algorithm interconnects the diverse characteristics of its deterministic and stochastic counterparts, while the estimation procedure assures the stability and invertibility requirements in the resulted models. For the practical implementation of the above, a five dimensional VARMA model is used for the description of a passenger vehicle, through the acquisition of noise–corrupted vertical acceleration measurements.
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Caraka, Rezzy Eko, Sakhinah Abu Bakar, and Muhammad Tahmid. "Rainfall forecasting multi kernel support vector regression seasonal autoregressive integrated moving average (MKSVR-SARIMA)." In THE 2018 UKM FST POSTGRADUATE COLLOQUIUM: Proceedings of the Universiti Kebangsaan Malaysia, Faculty of Science and Technology 2018 Postgraduate Colloquium. AIP Publishing, 2019. http://dx.doi.org/10.1063/1.5111221.

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Cai, Xia. "Vector Autoregressive Weighting Reversion Strategy for Online Portfolio Selection." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/616.

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Aiming to improve the performance of existing reversion based online portfolio selection strategies, we propose a novel multi-period strategy named “Vector Autoregressive Weighting Reversion” (VAWR). Firstly, vector autoregressive moving-average algorithm used in time series prediction is transformed into exploring the dynamic relationships between different assets for more accurate price prediction. Secondly, we design the modified online passive aggressive technique and advance a scheme to weigh investment risk and cumulative experience to update the closed-form of portfolio. Theoretical analysis and experimental results confirm the effectiveness and robustness of our strategy. Compared with the state-of-the-art strategies, VAWR greatly increases cumulative wealth, and it obtains the highest annualized percentage yield and sharp ratio on various public datasets. These improvements and easy implementation support the practical applications of VAWR.
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Qiao, Zongliang, Jianxin Zhou, Fengqi Si, Zhigao Xu, and Lei Zhang. "Fault diagnosis of slurry pH data base on autoregressive integrated moving average and least squares support vector machines." In 2013 9th International Conference on Natural Computation (ICNC). IEEE, 2013. http://dx.doi.org/10.1109/icnc.2013.6817959.

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Gholamhossein, Maryam, Ameneh Vatani, Najmeh Daroogheh, and K. Khorasani. "Prediction of the Jet Engine Performance Deterioration." In ASME 2012 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/imece2012-87936.

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This paper deals with performance deterioration modelling of a single spool gas turbine engine based on time-series methods. Towards this end, two univariate and multivariate methods, namely the Autoregressive Integrated Moving Average (ARIMA) and the Vector Autoregressive (VAR) schemes are applied to predict the Turbine Entry Temperature (TET) evolution during the flight cycles for maintenance purposes. In the VAR scheme, two engine process parameters i.e. the Turbine Entry Temperature (TET) and the Compressor Temperature are employed to achieve this prediction goal. The results show that employing multivariate modelling methods lead to better prediction horizons. For each method two scenarios are considered to demonstrate the effectiveness of the models.
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Vorwald, John, Alan Schwartz, Christopher Kent, and Phong Nguyen. "Forecasting Optimal Time-of-Arrival for Carrier Landings Using Prior Ship Motion." In ASME 2018 5th Joint US-European Fluids Engineering Division Summer Meeting. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/fedsm2018-83218.

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Sea-based aviation operations, such as carrier launch / recovery of aircraft, can be limited or interrupted by ship motion. Such operations may benefit from real time ship-motion forecasting, particularly in sea states above SS6, as unanticipated large motions may suddenly occur. Ship motion forecasting was optimized using an autoregressive moving average vector (ARMAV) model. The forecasting was accurate for approximately 25 seconds with accuracy evaluated using either correlation coefficient or root mean square error metrics. The ship motion data evaluated was simulation data generated by a ship motion prediction program for a generic CVN hull.
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Larbi, N., and J. Lardies. "Modal Parameters Estimation and Model Order Selection of a Structure Excited by a Random Force." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/vib-8095.

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Abstract A multivariate maximum likelihood procedure for the estimation of modal parameters is presented. The vibrating system is excited by a random force and sensors output only are used to estimate the natural frequencies and damping coefficients of the system. The method works in time domain and a vector autoregressive moving average (VARMA) process is used. The information about modal parameters is contained in the multivariate AR part, which is estimated using an iterative maximum likelihood algorithm. This algorithm uses a score technique and output data only. The order of the AR part is obtained via the Minimum Description Length associated with an instrumental variable procedure. Experimental results show the effectiveness of the method for model order selection and modal parameters estimation.
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I.A. Almghari, Khaled, and Adnan Elsibakhi. "Using Support Vector Machine "SVM" and Autoregressive Integrated Moving Average "ARIMA" to predict number of males and females who will be have Stroke at European Gaza Hospital." In المؤتمر العلمي الدولي العاشر. شبكة المؤتمرات العربية, 2019. http://dx.doi.org/10.24897/acn.64.68.405.

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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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Jiang, P., I. Bychkov, J. Liu, and A. Hmelnov. "Predicting of air pollutant concentrations based on spatio-temporal attention convolutional LSTM networks." In 1st International Workshop on Advanced Information and Computation Technologies and Systems 2020. Crossref, 2021. http://dx.doi.org/10.47350/aicts.2020.09.

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Forecasting of air pollutant concentration, which is influenced by air pollution accumulation, traffic flow and industrial emissions, has attracted extensive attention for decades. In this paper, we propose a spatio-temporal attention convolutional long short term memory neural networks (Attention-CNN-LSTM) for air pollutant concentration forecasting. Firstly, we analyze the Granger causalities between different stations and establish a hyperparametric Gaussian vector weight function to determine spatial autocorrelation variables, which is used as part of the input feature. Secondly, convolutional neural networks (CNN) is employed to extract the temporal dependence and spatial correlation of the input, while feature maps and channels are weighted by attention mechanism, so as to improve the effectiveness of the features. Finally, a depth long short term memory (LSTM) based time series predictor is established for learning the long-term and short-term dependence of pollutant concentration. In order to reduce the effect of diverse complex factors on LSTM, inherent features are extracted from historical air pollutant concentration data meteorological data and timestamp information are incorporated into the proposed model. Extensive experiments were performed using the Attention-CNNLSTM, autoregressive integrated moving average (ARIMA), support vector regression (SVR), traditional LSTM and CNN, respectively. The results demonstrated that the feasibility and practicability of Attention-CNN-LSTM on estimating CO and NO concentration.
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Reports on the topic "Vector autoregressive moving average"

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Block, H. W., N. A. Langberg, and D. S. Stoffer. Bivariate Exponential and Geometric Autoregressive and Autoregressive Moving Average Models. Fort Belvoir, VA: Defense Technical Information Center, March 1986. http://dx.doi.org/10.21236/ada185591.

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

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Swope, Gerald W. Likelihood Ratio Test for the Equivalence of Two Autoregressive Moving-Average Time Series. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada370599.

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