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Books on the topic 'ARIMA model30'

Author: Grafiati
Published: 11 September 2021
Last updated: 30 July 2025

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1

Choi, ByoungSeon. ARMA model identification. Springer-Verlag, 1992.

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2

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

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3

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

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4

Hella, Heikki. On robust ESACF indentification [sic] of mixed ARIMA models. Bank of Finland, 2003.

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5

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

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6

Marcucci, Benedetto. Modello Roma: Il grande bluff : perché la fama di Veltroni sindaco è campata in aria. Rubbettino, 2008.

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7

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

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8

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

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9

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 a
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10

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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11

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 univar
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12

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

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13

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

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14

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 articl
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15

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 interventio
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16

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
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17

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 a
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18

Mejías, Wilson. Predicción de precios de productos forestales usando modelos ARIMA. INFOR, 2022. http://dx.doi.org/10.52904/20.500.12220/32066.

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En el presente estudio se analizó el comportamiento de los valores nominales de dos productos forestales; trozas aserrables y madera aserrada (en pesos chilenos/m3) de pino radiata. En base a la información disponible en la Plataforma de Estadísticas Forestales (PEF) de INFOR, se construyeron series de tiempo de los precios de los productos seleccionados, considerando las regiones de Biobío, La Araucanía y Los Lagos, ajustando modelos de series de tiempo locales. Las series de tiempo de precios disponibles en la PEF son de carácter trimestral y abarcan desde el año 1980 hasta hoy.
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19

Forecasting Aggregated Vector ARMA Processes. Springer, 2012.

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20

Forecasting Aggregated Vector ARMA Processes. Springer, 1987.

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21

Forecasting Aggregated Vector ARMA Processes. Springer-Verlag, 1987.

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22

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

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23

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

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24

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

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25

Khan, Aman, and Kenneth A. Scorgie. Forecasting Government Budgets. The Rowman & Littlefield Publishing Group, 2022. https://doi.org/10.5040/9781666990355.

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Forecasting is integral to all governmental activities, especially budgetary activities. Without good and accurate forecasts, a government will not only find it difficult to carry out its everyday operations but will also find it difficult to cope with the increasingly complex environment in which it has to operate. This book presents, in a simple and easy to understand manner, some of the commonly used methods in budget forecasting, simple as well as advanced. The book is divided into three parts: It begins with an overview of forecasting background, forecasting process, and forecasting metho
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26

Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH. Wiley & Sons, Limited, John, 2018.

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27

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

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28

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 H
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29

Culinaria, Accademia. Friggitrice Ad Aria: 240 Ricette Italiane, Facili Sane e Veloci, Adatte Ad Ogni Modello, Consigli Ed Istruzioni per un Corretto Utilizzo. + Bonus Biscotti. Independently Published, 2021.

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30

Piccolo, Grazia. Friggitrice Ad Aria: Le Migliori 110 Ricette Italiane Autentiche, Facili e Salutari per Piatti Gourmet. Adatte Ad Ogni Modello con Consigli Ed Istruzioni + Bonus Biscotti. Independently Published, 2022.

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31

Culinaria, Accademia. Friggitrice Ad Aria: Le Migliori 110 Ricette Italiane Autentiche, Facili e Salutari per Piatti Gourmet. Adatte Ad Ogni Modello con Consigli Ed Istruzioni + Bonus Biscotti. Independently Published, 2022.

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32

Piccolo, Grazia. Friggitrice Ad Aria: Le Migliori 110 Ricette Italiane Autentiche, Facili e Salutari per Piatti Gourmet. Adatte Ad Ogni Modello con Consigli Ed Istruzioni + Bonus Biscotti. Independently Published, 2022.

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33

Friggitrice Ad Aria: 240 Ricette Italiane, Facili Sane e Veloci, Adatte Ad Ogni Modello, Consigli Ed Istruzioni per un Corretto Utilizzo+ Bonus Biscotti. Edizione a Colori. Independently Published, 2021.

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34

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

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35

Paluani, Benedetta. Friggitrice Ad Aria: Ricettario Definitivo per Principianti + 600 Ricette Facili, Veloci e Gustose + 12 Consigli per Cotture Perfette + Piatti per Vegetariani e Vegani, Compatibile con Tutti I Modelli. Independently Published, 2022.

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36

Russo, Lorita, and Emma Milani. Friggitrice Ad Aria Zero Sbatti: 2000+ Nuove Ricette Italiane, Facili e Veloci Da Preparare con Qualsiasi Modello. Indice Alfabetico per Consultarle All'Istante and 21 Segreti per Piatti Infallibili! Independently Published, 2022.

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37

Jaimes Suárez, Yeirme Yaneth, Genaro Andrés Agudelo Castañeda, Eliana Yadira Báez Daza, Felipe Montealegre Bustos, Gersaín Antonio Rengifo Estrada, and Jairo Rojas Molina. Modelo productivo para el cultivo de cacao (Theobroma cacao L.) en el departamento de Santander (2a edición). Corporación Colombiana de Investigación Agropecuaria (Agrosavia), 2022. http://dx.doi.org/10.21930/agrosavia.model.7405538.

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Santander es el primer productor de cacao en Colombia y con un rendimiento promedio de 600 kg/ha de cacao seco, superior a otros departamentos. Sin embargo, esos indicadores de productividad no le garantizan una rentabilidad, ni la sostenibilidad que permita que la producción de cacao sea aspiracional para todos los jóvenes rurales que emigran a la ciudad en búsqueda de alternativas productivas, ya que no hay una mejora en su calidad de vida. Estamos seguros de que productores juiciosos, dedicados y que aplican la tecnología disponible para el sector cacaotero tienen rendimientos superiores a
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38

Cardin, Denise. Ricettario per la Tua Friggitrice Ad Aria - Compatibile con Tutti I Modelli: 800 Facili e Veloci Ricette Che Dall'Antipasto Al Dolce Ti Aiuteranno Ad Ottenere una Frittura Perfetta, Sana e Gustosa. Independently Published, 2022.

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39

Ricettario per la Tua Friggitrice Ad Aria - Compatibile con Tutti I Modelli: 800 Facili e Veloci Ricette Che Dall'Antipasto Al Dolce Ti Aiuteranno Ad Ottenere una Frittura Perfetta, Sana e Gustosa. Independently Published, 2022.

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40

Arcila Cardona, Ángela María, Gloria Patricia Castillo Urquiza, Lumey Pérez Artiles, Carlos Alberto Abaunza González, Marlon José Yacomelo Hernández, and Rommel Igor León Pacheco. Modelo productivo de mango de azúcar (Mangifera indica L.) para el departamento del Magdalena. Corporación Colombiana de Investigación Agropecuaria (Agrosavia), 2022. http://dx.doi.org/10.21930/agrosavia.model.7405170.

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El mango de azúcar es un fruto pequeño, de color amarillo a rojo en su madurez, aroma y sabor dulce y agradable y bajo contenido de fibra. Todas estas características lo convierten en una fruta muy apetecida en todo el país, un postre saludable, del tamaño adecuado para ser llevado y consumido en el camino, en la lonchera, en el trabajo. Un fruto de mango de azúcar contiene la cantidad justa de energía para animarnos en la jornada y dar placer a los sentidos. Entre todos los mangos de azúcar de Colombia, ninguno se iguala al del Magdalena, su cuna, su nicho, el lugar que le da el color, olor,
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