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Books on the topic 'Models arima'

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Published: 4 June 2021
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1

Meyler, Aidan. Forecasting Irish inflation using ARIMA models. Dublin: Central Bank of Ireland, Economic Analysis, Research and Publications Department, 1998.

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2

Fritzer, Friedrich. Forecasting Austrian HICP and its components using VAR and ARIMA models. Wien: Oesterreichische Nationalbank, 2002.

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3

Yŏ, Un-bang. Sŭngpŏp kyejŏl ARIMA mohyŏng ŭi kujo sikpyŏl pangbŏp. Sŏul Tʻŭkpyŏlsi: Hang̕uk Kaebal Yŏng̕uwŏn, 1985.

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4

Reid, Abigail-Kate, and Nick Allum. Learn About Time Series ARIMA Models in Stata With Data From the USDA Feed Grains Database (1876–2015). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2020. http://dx.doi.org/10.4135/9781529710281.

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5

Reid, Abigail-Kate, and Nick Allum. Learn About Time Series ARIMA Models in Stata With Data From the NOAA Global Climate at a Glance (1910–2015). 1 Oliver's Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2020. http://dx.doi.org/10.4135/9781529710380.

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6

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

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7

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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8

Routis, J. Bayesian analysis of ARMA models. Manchester: UMIST, 1997.

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9

Shimizu, Kenichi. Bootstrapping Stationary ARMA-GARCH Models. Wiesbaden: Vieweg+Teubner, 2010. http://dx.doi.org/10.1007/978-3-8348-9778-7.

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10

service), SpringerLink (Online, ed. Bootstrapping Stationary ARMA-GARCH Models. Wiesbaden: Vieweg+Teubner Verlag / Springer Fachmedien Wiesbaden GmbH, Wiesbaden, 2010.

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11

Kuersteiner, Guido M. Optimal instrumental variables estimation for ARMA models. Cambridge, Mass: Dept. of Economics, Massachusetts Institute of Technology, 1999.

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12

Smith, Jeremy. Comparing the bias and misspecification in Arfima models. Coventry: Warwick University, Department of Economics, 1995.

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13

Gilbert, Paul Douglas. State space and ARMA models: An overview of the equivalence. [Ottawa]: Bank of Canada, 1993.

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14

Özgüler, Verda Canbey. İş arama teorisi, sosyal ağlar ve internet. Eskişehir: Anadolu Üniversitesi, 2007.

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15

W, Evans George. The algebra of ARMA processes and the structure of ARMA solutions to a general linear model with rational expectations. Stanford, Calif: Institute for Mathematical Studies in the Social Sciences, Stanford University, 1985.

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16

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

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17

Robuste Schätzung von ARMA-Modellen unter Verwendung von robust geschätzten Autokovarianzen. Frankfurt am Main: P. Lang, 1994.

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18

Azencott, Robert. Series of irregular observations: Forecasting and model building. New York: Springer-Verlag, 1986.

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19

Case in Giappone: Tadao Ando, Hiroyuki Arima, Shigeru Ban, Shuhei Endo, Masaki Endoh+Masahiro Ikeda, Kei'Ichi Irie, Arata Isozaki, Toyo Ito, Waro Kishi, Masao Koizumi, Katsufumi Kubota, Kengo Kuma, Kisho Kurokawa, Satoshi Okada, Kazuyo Sejima, Naoto Yaegashi. Milano: Electa, 2005.

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20

The Duchy of Warsaw, 1807-1815: A Napoleonic outpost in Central Europe. London: Bloomsbury Academic, 2016.

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21

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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22

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

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23

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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24

McCleary, Richard, David McDowall, and Bradley J. Bartos. Forecasting. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780190661557.003.0004.

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Chapter 4 downplays forecasting’s role in the design and analysis of time series experiments and emphasizes its potential abuses. While the “best” ARIMA model will outperform other forecasting models in the short and medium-run, long-horizon ARIMA forecasts grow increasingly inaccurate with diminished utility to the forecaster. Although the principles of forecasting help provide deeper insight into the nature of ARIMA models and modeling, the forecasts themselves are ordinarily of limited practical value. Forecasting can provide useful guidance to analysts choosing between two competing univariate models. While forecasting accuracy is only one of many criteria that might be considered, other things being equal, it is fair to say that a statistically adequate model of a process should provide reasonable forecasts of the future. Forecast accuracy depends on a host of factors, many of which lie outside the grasp of model adequacy. More important, forecast accuracy has no universally accepted metric.
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25

Time Series ARIMA Models and the USDA Feed Grains Database (1876–2015): U.S. Oats Yield per Acre. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2017. http://dx.doi.org/10.4135/9781473995598.

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26

Time Series ARIMA Models and the NOAA Global Climate at a Glance (1910–2015): Average Land Temperatures in Asia. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2017. http://dx.doi.org/10.4135/9781473995321.

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27

Pevehouse, Jon, and Jason D. Brozek. Time‐Series Analysis. Edited by Janet M. Box-Steffensmeier, Henry E. Brady, and David Collier. Oxford University Press, 2009. http://dx.doi.org/10.1093/oxfordhb/9780199286546.003.0019.

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This article discusses time-series methods such as simple time-series regressions, ARIMA models, vector autoregression (VAR) models, and unit root and error correction models (ECM). It specifically presents a brief history of time-series analysis before moving to a review of the basic time-series model. It then describes the stationary models in univariate and multivariate analyses. The nonstationary models of each type are addressed. In addition, various issues regarding the analysis of time series including data aggregation and temporal stability are considered. Before concluding, the article briefly reports the time-series techniques in the context of panel data. In general, time-series analysis can help improve the understanding of the political world.
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28

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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29

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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30

Choi, ByoungSeon. ARMA Model Identification. Springer, 2012.

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31

Leeuw, Jan Van Der. Maximum Likelihood Estimation of Exact ARMA Models. Tilburg University Press, 1997.

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32

Niu, Xufeng. Space-time ARMA models for satellite ozone data. 1991.

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33

Modelos Arima-Arch: Algunas aplicaciones a las series de tiempo financieras. Medellín: Universidad de Medellín, 2008.

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34

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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35

Paolella, Marc S. Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH. Wiley & Sons, Incorporated, John, 2018.

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36

Prediction and Estimation in Arma Models (Skriftserie-Publications / Statistiska Institutionen, Ghoteb). Coronet Books, 1986.

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37

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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38

Estimating the Degrees of an Arma Model. Correspondence Analysis and Gaussian Ordination. Physica-Verlag, 1985.

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39

Ivanovitch, Roman. The Brilliant Style. Edited by Danuta Mirka. Oxford University Press, 2014. http://dx.doi.org/10.1093/oxfordhb/9780199841578.013.0013.

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The brilliant style, described loosely by Leonard Ratner as rapid passages for virtuoso display, has been a mainstay of modern topic theory, often invoked in conjunction with the singing style to account for the basic contrastive mechanism of the classical style. This chapter explores some contextual bases for the topic, suggesting that eighteenth-century linguistic usage can offer useful nuance and proposing a topical home in the genre of the concerto. Illustrations relate to the concerto, aria, symphony, and quartet, and examine both keyboard and string virtuosity. At the heart of the brilliant style is a set of propensities for public and theatrical modes, tied to a sense of occasion; it can highlight tensions between composer and performer, and relates directly to our constructions of the active “persona” in a composition or performance.
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40

Govrin, Nurith. Ketivat ha-arets: Aratsot ve-arim al mapat ha-sifrut ha-Ivrit. Karmel, 1998.

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41

Denis, Philippe. Case Study: Memory Work with Children Affected by HIV/AIDS in South Africa. Edited by Donald A. Ritchie. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780195339550.013.0011.

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This article focuses on working with children affected by HIV/AIDS in South Arica. In the early years of the AIDS epidemic, relief organizations focused their efforts on the material needs of children, but their psychological and emotional needs are no less important. Recognizing this, the Sinomlando Centre for Oral History and Memory Work in Africa, a research and community development center located at the University of KwaZulu-Natal, in Pietermaritzburg South Africa, has pioneered a model of psychosocial intervention for children in grief—particularly but not exclusively in the context of HIV/AIDS. This model uses the methodology of oral history in a novel manner, combined with other techniques such as life story work and narrative therapy. During the early years of the project, the model followed for the family visits was the oral history interview. A discussion on caregiver as the narrator and skills required in memory work especially in these cases concludes this article.
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42

Luca, Canali, and Lelli Emanuele, eds. Arma virumque--: Studi di poesia e storiografia in onore di Luca Canali. Pisa: Istituti editoriali e poligrafici internazionali, 2002.

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43

Sartorio, Antonio. Giulio Cesare in Egitto. Edited by Craig Monson. A-R Editions, 1991. http://dx.doi.org/10.31022/y2-012.

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Scarcely any operas from the period between Cavalli and Alessandro Scarlatti have been published in modern editions. Antonio Sartorio's Giulio Cesare in Egitto, first performed in Venice in 1676, reduces this gap. The present edition reconstructs the versions from both the Venetian premiere and the Naples revival of 1680. The opera illustrates the flexible intermingling, characteristic of this period, of dramatic recitative and the incipient da capo aria, and also serves as an interesting foil for Handel's popular Giulio Cesare, which was based on the same libretto and, possibly, even some of Sartorio's music.
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44

Melamed, Daniel R. The Musical Topic of the Mass in B Minor. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190881054.003.0003.

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If there is a fundamental musical subject of Johann Sebastian Bach’s Mass in B Minor, a compositional problem the work explores, it is the tension between two styles cultivated in church music of Bach’s time. One style was modern and drew on up-to-date music such as the instrumental concerto and the opera aria. The other was old-fashioned and fundamentally vocal, borrowing and adapting the style of Giovanni Pierluigi da Palestrina, his sixteenth-century contemporaries, and his seventeenth-century imitators. The movements that make up Bach’s Mass can be read as exploring the entire spectrum of possibilities offered by these two styles (the modern and the antique), ranging from movements purely in one or the other to a dazzling variety of ways of combining the two. The work illustrates a fundamental opposition in early-eighteenth-century sacred music that Bach confronts and explores in the Mass.
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45

Mirchandani, Sharon. Modern Dance and the MGM Recordings. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037313.003.0002.

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This chapter focuses on Marga Richter's success with her modern dance scores and MGM music recordings during the period 1951–1960. After graduating from Julliard Graduate School, Richter moved to an apartment on 308 West 107th Street. In New York City, she was able to attend fine traditional and new music concerts, visit museums, and partake in the cultural life of the city. However, earning a living was a significant concern. Fortunately, Richter's music had drawn the attention of choreographer James Waring. This chapter first considers Richter's studies in New York and her marriage to Alan Skelly before turning to her early modern dance, piano, and orchestral works as well as compositions for children. It also examines Richter's beliefs and views on women's roles, along with her teaching pieces. Finally, it looks at Richter's concert music compositions commissioned by Edward Cole for recording by MGM, including Sonata for Piano (1954), Lament for string orchestra (1956), and Aria and Toccata for viola and string orchestra (1957).
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46

Georgescu, Laurentiu Stelian. Contributia contextuala a parintelui profesor Petru Rezus la dezvoltarea dogmaticii in teologia ortodoxa romana. Editura Universitara, 2020. http://dx.doi.org/10.5682/9786062811969.

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Lucrarea elaborata de parintele Georgescu Stelian- Laurentiu raspunde exigentelor stiintifice actuale, specific domeniului Teologie, exprimand teologic o tema de interes actual pentru cercetarea teologica dogmatica din Romania. Titlul lucrarii este inedit si exprima parcursul si contributia teologica, contextuala a parintelui Petru Rezus pe parcursul a mai bine de jumatate de secol. In ceea ce priveste caracterul stiintific, lucrarea se integreaza in exigentele formale ale stadiului formativ pe care il reprezinta, fiind fundamentata pe surse, pe documente de arhiva si pe o bogata bibliografie actuala, in concordanta cu tema si cu evolutia ideilor din aria pe care o cerceteaza. In viziunea autorului, lucrarea nu este doar un periplu prin cadrele Teologiei Dogmatice cu rezultate ,,temporale, spatiale si modale”, ci este o sinteza cu implicatii hristologice, pnevmatologice, ecleziologice, trinitare, antropologice, sinteza care a reprezentat centrul preocuparilor teologice a parintelui Rezus
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47

Ghosh, Subir. Asymptotics, Nonparametrics, and Time Series (Statistics: a Series of Textbooks and Monogrphs). CRC, 1999.

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