UNIVARIATE TIME SERIES FORECASTING. BOX JENKINS METHODOLOGY: ARIMA MODELS. Examples with R PDF Download
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Author: Felicidad MARQUÉS Publisher: ISBN: Category : Languages : en Pages : 230
Book Description
This book develops the Box and Jenkins methodology for the prediction of time series through the ARIMA models. The book begins by introducing the concepts needed to make univariate time series predictions. Next, the identification, estimation and prediction of the ARIMA models is deepened, both in the non-seasonal field and in the seasonal field. An important part of the content is the automatic prediction methods, including the use of neural networks and the space of the states to obtain improved predictions of time series. The intervention models that collect the effects of atypicalities in obtaining predictions are discussed below. Finally, the transfer function models or ARIMAX models that use external continuous regressors to guide the predictions of a time series are considered. A great variety of examples and exercises solved with R. are presented.
Author: Felicidad MARQUÉS Publisher: ISBN: Category : Languages : en Pages : 230
Book Description
This book develops the Box and Jenkins methodology for the prediction of time series through the ARIMA models. The book begins by introducing the concepts needed to make univariate time series predictions. Next, the identification, estimation and prediction of the ARIMA models is deepened, both in the non-seasonal field and in the seasonal field. An important part of the content is the automatic prediction methods, including the use of neural networks and the space of the states to obtain improved predictions of time series. The intervention models that collect the effects of atypicalities in obtaining predictions are discussed below. Finally, the transfer function models or ARIMAX models that use external continuous regressors to guide the predictions of a time series are considered. A great variety of examples and exercises solved with R. are presented.
Author: Alan Pankratz Publisher: John Wiley & Sons ISBN: Category : Mathematics Languages : en Pages : 584
Book Description
Explains the concepts and use of univariate Box-Jenkins/ARIMA analysis and forecasting through 15 case studies. Cases show how to build good ARIMA models in a step-by-step manner using real data. Also includes examples of model misspecification. Provides guidance to alternative models and discusses reasons for choosing one over another.
Author: George E. P. Box Publisher: ISBN: Category : Business & Economics Languages : en Pages : 628
Book Description
This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control. Features sections on: recently developed methods for model specification,such as canonical correlation analysis and the use of model selection criteria; results on testing for unit root nonstationarity in ARIMA processes; the state space representation of ARMA models and its use for likelihood estimation and forecasting; score test for model checking; and deterministic components and structural components in time series models and their estimation based on regression-time series model methods.
Author: Jason Brownlee Publisher: Machine Learning Mastery ISBN: Category : Mathematics Languages : en Pages : 359
Book Description
Time series forecasting is different from other machine learning problems. The key difference is the fixed sequence of observations and the constraints and additional structure this provides. In this Ebook, finally cut through the math and specialized methods for time series forecasting. Using clear explanations, standard Python libraries and step-by-step tutorials you will discover how to load and prepare data, evaluate model skill, and implement forecasting models for time series data.
Author: Perez M. Publisher: Createspace Independent Publishing Platform ISBN: 9781534860919 Category : Languages : en Pages : 192
Book Description
Econometrics Toolbox(TM) provides functions for modeling economic data. You can select and calibrate economic models for simulation and forecasting. For time series modeling and analysis, the toolbox includes univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis. It also provides methods for modeling economic systems using state-space models and for estimating using the Kalman filter. You can use a variety of diagnostic functions for model selection, including hypothesis, unit root, and stationarity tests.. This book especially developed ARIMA and ARIMAX models acfross BOX-JENKINS methodology
Author: Chris Chatfield Publisher: CRC Press ISBN: 0203491688 Category : Mathematics Languages : en Pages : 349
Book Description
Since 1975, The Analysis of Time Series: An Introduction has introduced legions of statistics students and researchers to the theory and practice of time series analysis. With each successive edition, bestselling author Chris Chatfield has honed and refined his presentation, updated the material to reflect advances in the field, and presented interesting new data sets. The sixth edition is no exception. It provides an accessible, comprehensive introduction to the theory and practice of time series analysis. The treatment covers a wide range of topics, including ARIMA probability models, forecasting methods, spectral analysis, linear systems, state-space models, and the Kalman filter. It also addresses nonlinear, multivariate, and long-memory models. The author has carefully updated each chapter, added new discussions, incorporated new datasets, and made those datasets available for download from www.crcpress.com. A free online appendix on time series analysis using R can be accessed at http://people.bath.ac.uk/mascc/TSA.usingR.doc. Highlights of the Sixth Edition: A new section on handling real data New discussion on prediction intervals A completely revised and restructured chapter on more advanced topics, with new material on the aggregation of time series, analyzing time series in finance, and discrete-valued time series A new chapter of examples and practical advice Thorough updates and revisions throughout the text that reflect recent developments and dramatic changes in computing practices over the last few years The analysis of time series can be a difficult topic, but as this book has demonstrated for two-and-a-half decades, it does not have to be daunting. The accessibility, polished presentation, and broad coverage of The Analysis of Time Series make it simply the best introduction to the subject available.
Author: James Durbin Publisher: Oxford University Press ISBN: 9780198523543 Category : Business & Economics Languages : en Pages : 280
Book Description
State space time series analysis emerged in the 1960s in engineering, but its applications have spread to other fields. Durbin (statistics, London School of Economics and Political Science) and Koopman (econometrics, Free U., Amsterdam) extol the virtues of such models over the main analytical system currently used for time series data, Box-Jenkins' ARIMA. What distinguishes state space time models is that they separately model components such as trend, seasonal, regression elements and disturbance terms. Part I focuses on traditional and new techniques based on the linear Gaussian model. Part II presents new material extending the state space model to non-Gaussian observations. c. Book News Inc.
Author: Felicidad MARQUÉS
Author: Felicidad MARQUÉS
Author: Walter Vandaele
Author: Alan Pankratz
Author: George E. P. Box
Author: Jason Brownlee
Author: Oliver Duncan Anderson
Author: Perez M.
Author: Chris Chatfield
Author: James Durbin
Author: Robert H. Shumway