Prediction and Estimation in ARMA Models

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Publisher : Coronet Books
ISBN 13 :
Total Pages : 138 pages
Book Rating : 4.F/5 ( download)

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Book Synopsis Prediction and Estimation in ARMA Models by : Helgi Tómasson

Download or read book Prediction and Estimation in ARMA Models written by Helgi Tómasson and published by Coronet Books. This book was released on 1986 with total page 138 pages. Available in PDF, EPUB and Kindle. Book excerpt:

ARMA Model Identification

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Publisher : Springer
ISBN 13 :
Total Pages : 244 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis ARMA Model Identification by : ByoungSeon Choi

Download or read book ARMA Model Identification written by ByoungSeon Choi and published by Springer. This book was released on 1992 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Robust Estimation and Prediction Using an ARMA Model

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Publisher :
ISBN 13 :
Total Pages : 76 pages
Book Rating : 4.:/5 (961 download)

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Book Synopsis Robust Estimation and Prediction Using an ARMA Model by : Julian Robert Trujillo

Download or read book Robust Estimation and Prediction Using an ARMA Model written by Julian Robert Trujillo and published by . This book was released on 1987 with total page 76 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Time Series Models

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Author :
Publisher : John Wiley & Sons
ISBN 13 :
Total Pages : 248 pages
Book Rating : 4.3/5 (9 download)

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Book Synopsis Time Series Models by : Andrew C. Harvey

Download or read book Time Series Models written by Andrew C. Harvey and published by John Wiley & Sons. This book was released on 1981 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Time Series Analysis: Forecasting & Control, 3/E

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Author :
Publisher : Pearson Education India
ISBN 13 : 9788131716335
Total Pages : 620 pages
Book Rating : 4.7/5 (163 download)

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Book Synopsis Time Series Analysis: Forecasting & Control, 3/E by :

Download or read book Time Series Analysis: Forecasting & Control, 3/E written by and published by Pearson Education India. This book was released on 1994-09 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a complete revision of a classic, seminal, and authoritative text that has been the model for most books on the topic written since 1970. 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.

A Unified Approach to ARMA Model Identification and Preliminary Estimation

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Publisher :
ISBN 13 :
Total Pages : 24 pages
Book Rating : 4.:/5 (227 download)

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Book Synopsis A Unified Approach to ARMA Model Identification and Preliminary Estimation by : G. Tunnicliffe Wilson

Download or read book A Unified Approach to ARMA Model Identification and Preliminary Estimation written by G. Tunnicliffe Wilson and published by . This book was released on 1983 with total page 24 pages. Available in PDF, EPUB and Kindle. Book excerpt: This reprint reviews several different methods for identifying the orders of autoregressive-moving average models for time series data. The case is made that these have a common basis, and that a unified approach may be found in the analysis of a matrix G, defined to be the covariance matrix of forecast values. The estimation of this matrix is considered, emphasis being placed on the use of high order autoregression to approximate the predictor coefficients. Statistical procedures are proposed for analysing G, and identifying the model orders. A simulation example and three sets of real data are used to illustrate the procedure, which appears to be a very useful tool for order identification and preliminary model estimation. Additional keywords: Yule-Walker equations; Dubin-Levinson algorithm; prediction spaces; Choleski factorization. (Author).

Estimating a Multivariate ARMA Model with Mixed-frequency Data

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Publisher :
ISBN 13 :
Total Pages : 124 pages
Book Rating : 4.3/5 (97 download)

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Book Synopsis Estimating a Multivariate ARMA Model with Mixed-frequency Data by : Peter A. Zadrozny

Download or read book Estimating a Multivariate ARMA Model with Mixed-frequency Data written by Peter A. Zadrozny and published by . This book was released on 1990 with total page 124 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Forecasting models – an overview with the help of R software

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Publisher : international Journal of Statistics and Medical Informatics
ISBN 13 : 1081552808
Total Pages : 101 pages
Book Rating : 4.0/5 (815 download)

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Book Synopsis Forecasting models – an overview with the help of R software by : Editor IJSMI

Download or read book Forecasting models – an overview with the help of R software written by Editor IJSMI and published by international Journal of Statistics and Medical Informatics. This book was released on 2019-07-20 with total page 101 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting models – an overview with the help of R software Preface Forecasting models involves predicting the future values of a particular series of data which is mainly based on the time domain. Forecasting models are widely used in the fields such as financial markets, demand for a product and disease outbreak. The objective of the forecasting model is to reduce the error in the forecasting. Most of the Forecasting models are based on time series, a statistical concept which involves Moving Averages, Auto Regressive Integrated Moving Averages (ARIMA), Exponential smoothing and Generalized Auto Regressive Conditional Heteroscedastic (GARCH) Models. Forecasting models which we deal in this book will be explorative forecasting models which take into account the past data to predict the future values. Current day forecasting models uses advanced techniques such as Machine Learning and Deep Learning Algorithms which are more robust and can handle high volume of data. This book starts with the overview of forecasting and time series concepts and moves on to build forecasting models using different time series models. Examples related to forecasting models which are built based on Machine learning also covered. The book uses R statistical software package, an open source statistical package to build the forecasting models. Editor International Journal of Statistics and Medical Informatics www.ijsmi.com/book.php https://www.amazon.co.uk/dp/B07VFY53B1

Developments in Time Series Analysis

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Publisher : CRC Press
ISBN 13 : 9780412492600
Total Pages : 466 pages
Book Rating : 4.4/5 (926 download)

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Book Synopsis Developments in Time Series Analysis by : T. Subba Rao

Download or read book Developments in Time Series Analysis written by T. Subba Rao and published by CRC Press. This book was released on 1993-07-01 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume contains 27 papers, written by time series analysts, dealing with statistical theory, methodology and applications. The emphasis is on the recent developments in the analysis of linear, onlinear (non-Gaussian), stationary and nonstationary time series. The topics include cointegration, estimation and asymptotic theory, Kalman filtering, nonparametric statistical inference, long memory models, nonlinear models, spectral analysis of stationary and nonstationary processes. Quite a number of papers are devoted to modelling and analysis of real time series, and the econometricians, mathematical statisticians, communications engineers and scientists who use time series techniques and Fourier analysis should find the papers in this volume useful.

Introduction to Time Series and Forecasting

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Publisher : Springer
ISBN 13 : 9783319298528
Total Pages : 425 pages
Book Rating : 4.2/5 (985 download)

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Book Synopsis Introduction to Time Series and Forecasting by : Peter J. Brockwell

Download or read book Introduction to Time Series and Forecasting written by Peter J. Brockwell and published by Springer. This book was released on 2016-09-20 with total page 425 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is aimed at the reader who wishes to gain a working knowledge of time series and forecasting methods as applied to economics, engineering and the natural and social sciences. It assumes knowledge only of basic calculus, matrix algebra and elementary statistics. This third edition contains detailed instructions for the use of the professional version of the Windows-based computer package ITSM2000, now available as a free download from the Springer Extras website. The logic and tools of time series model-building are developed in detail. Numerous exercises are included and the software can be used to analyze and forecast data sets of the user's own choosing. The book can also be used in conjunction with other time series packages such as those included in R. The programs in ITSM2000 however are menu-driven and can be used with minimal investment of time in the computational details. The core of the book covers stationary processes, ARMA and ARIMA processes, multivariate time series and state-space models, with an optional chapter on spectral analysis. Many additional special topics are also covered. New to this edition: A chapter devoted to Financial Time Series Introductions to Brownian motion, Lévy processes and Itô calculus An expanded section on continuous-time ARMA processes Peter J. Brockwell and Richard A. Davis are Fellows of the American Statistical Association and the Institute of Mathematical Statistics and elected members of the International Statistics Institute. Richard A. Davis is the current President of the Institute of Mathematical Statistics and, with W.T.M. Dunsmuir, winner of the Koopmans Prize. Professors Brockwell and Davis are coauthors of the widely used advanced text, Time Series: Theory and Methods, Second Edition (Springer-Verlag, 1991). From reviews of the first edition:“/div> This book, like a good science fiction novel, is hard to put down.... Fascinating examples hold one’s attention and are taken from an astonishing variety of topics and fields.... Given that time series forecasting is really a simple idea, it is amazing how much beautiful mathematics this book encompasses. Each chapter is richly filled with examples that serve to illustrate and reinforce the basic concepts. The exercises at the end of each chapter are well designed and make good use of numerical problems. Combined with the ITSM package, this book is ideal as a textbook for the self-study student or the introductory course student. Overall then, as a text for a university-level course or as a learning aid for an industrial forecaster, I highly recommend the book. —SIAM Review In addition to including ITSM, the book details all of the algorithms used in the package—a quality which sets this text apart from all others at this level. This is an excellent idea for at least two reasons. It gives the practitioner the opportunity to use ITSM more intelligently by providing an extra source of intuition for understanding estimation and forecasting, and it allows the more adventurous practitioners to code their own algorithms for their individual purposes.... Overall I find Introduction to Time Series and Forecasting to be a very useful and enlightening introduction to time series. —Journal of the American Statistical Association divThe emphasis is on hands-on experience and the friendly software that accompanies the book serves the purpose admirably.... The authors should be congratulated for making the subject accessible and fun to learn. The book is a pleasure to read and highly recommended. I regard it as the best introductory text in town. —Short Book Reviews, International Statistical Review

Time Series Analysis

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Publisher : San Francisco : Holden-Day
ISBN 13 :
Total Pages : 584 pages
Book Rating : 4.3/5 (91 download)

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Book Synopsis Time Series Analysis by : George E. P. Box

Download or read book Time Series Analysis written by George E. P. Box and published by San Francisco : Holden-Day. This book was released on 1970 with total page 584 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book is concerned with the building of models for discrete time series and dynamic systems. It describes in detail how such models may be used to obtain optimal forecasts and optimal control action. All the techniques are illustrated with examples using economic and industrial data. In Part 1, models for stationary and nonstationary time series are introduced, and their use in forecasting is discussed and exemplified. Part II is devoted to model building, and procedures for model identification, estimation, and checking which are then applied to the forecasting of seasonal time series. Part III is concerned with the building of transfer function models relating the input and output of a dynamic system computed by noise. In Part IV it is shown how transfer function and time series models may be used to design optimal feedback and feedforward control schemes. Part V contains an outline of computer programs useful in making the needed calculations and also includes charts and tables of value in identifying the models.

Time Series ARMA Model Identification by Estimating Information

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Publisher :
ISBN 13 :
Total Pages : 8 pages
Book Rating : 4.:/5 (227 download)

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Book Synopsis Time Series ARMA Model Identification by Estimating Information by : Emanuel Parzen

Download or read book Time Series ARMA Model Identification by Estimating Information written by Emanuel Parzen and published by . This book was released on 1983 with total page 8 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statisticians, economists, and system engineers are becoming aware that to identify models for time series and dynamic systems, information theoretic ideas can play a valuable (and unifying) role. Models for time series Y(t) can be formulated as hypotheses concerning the information about Y(t) given various bases involving past, current, and future values of Y(.) and related time series X(.). To determine sets of variables that are sufficient to forecast Y(t), and especially to determine an ARMA model for Y(t), an approach is presented which estimates and compares various information increments. The author discusses how to non-parametrically estimate the MA(infinity) representation, and use it to form estimators of the many information numbers that might compare to identify an ARMA model for a univariate time series. (Author).

Prediction in ARMA Models with GARCH in Mean Effects

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Publisher :
ISBN 13 :
Total Pages : 21 pages
Book Rating : 4.:/5 (247 download)

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Book Synopsis Prediction in ARMA Models with GARCH in Mean Effects by : Menelaos Karanasos

Download or read book Prediction in ARMA Models with GARCH in Mean Effects written by Menelaos Karanasos and published by . This book was released on 1999 with total page 21 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Time Series Analysis With Matlab

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Publisher : CreateSpace
ISBN 13 : 9781502346384
Total Pages : 192 pages
Book Rating : 4.3/5 (463 download)

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Book Synopsis Time Series Analysis With Matlab by : Mara Prez

Download or read book Time Series Analysis With Matlab written by Mara Prez and published by CreateSpace. This book was released on 2014-09-12 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: MATLAB Econometrics Toolbox provides functions for modeling economic data You can select and calibrate economic models for simulation and forecasting Time series capabilities include univariate ARMAX/GARCH composite models with several GARCH variants, multivariate VARMAX models, and cointegration analysis The toolbox provides Monte Carlo methods for simulating systems of linear and nonlinear stochastic differential equations and a variety of diagnostics for model selection, including hypothesis, unit root, and stationarity tests.This book develops, among others, the following topics:Conditional Mean Models for Stationary Processes Specify Conditional Mean Models Using ARIMA Autoregressive Model AR(p) Model AR Model with No Constant Term AR Model with Nonconsecutive Lags AR Model with Known Parameter Values AR Model with a t Innovation Distribution Moving Average Model MA(q) Model Invertibility of the MA Model MA Model Specifications MA Model with No Constant Term MA Model with Nonconsecutive Lags MA Model with Known Parameter Values MA Model with a t Innovation Distribution Autoregressive Moving Average ModelARMA(p,q) Model Stationarity and Invertibility of the ARMA Model ARMA Model Specifications ARMA Model with No Constant Term ARMA Model with Known Parameter Values ARIMA Model ARIMA Model Specifications ARIMA Model with Known Parameter Values Multiplicative ARIMA Model Multiplicative ARIMA Model Specifications Seasonal ARIMA Model with No Constant Term Seasonal ARIMA Model with Known Parameter Values Specify Multiplicative ARIMA Model ARIMA Model Including Exogenous Covariates ARIMAX(p,D,q) Model ARIMAX Model Specifications Specify Conditional Mean Model Innovation Distribution Specify Conditional Mean and Variance Model Impulse Response Function Plot Impulse Response Function Box-Jenkins Differencing vs ARIMA Estimation Maximum Likelihood Estimation for Conditional Mean ModelsConditional Mean Model Estimation with Equality Constraints Initial Values for Conditional Mean Model Estimation Optimization Settings for Conditional Mean Model Estimation Estimate Multiplicative ARIMA Model Model Seasonal Lag Effects Using Indicator Variables Forecast IGD Rate Using ARIMAX Model Estimate Conditional Mean and Variance Models Choose ARMA Lags Using BIC Infer Residuals for Diagnostic Checking Monte Carlo Simulation of Conditional Mean Models Presample Data for Conditional Mean Model Simulation Transient Effects in Conditional Mean Model Simulations Simulate Stationary Processes Simulate an AR Process Simulate an MA Process Simulate Trend-Stationary and Difference-Stationary Processes Simulate Multiplicative ARIMA Models Simulate Conditional Mean and Variance Models Monte Carlo Forecasting of Conditional Mean Models Monte Carlo Forecasts MMSE Forecasting of Conditional Mean Models Forecast Error Convergence of AR Forecasts Forecast Multiplicative ARIMA Model Forecast Conditional Mean and Variance Model

ARMA (Autoregressive-Moving Average) Modeling

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Publisher :
ISBN 13 :
Total Pages : 89 pages
Book Rating : 4.:/5 (227 download)

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Book Synopsis ARMA (Autoregressive-Moving Average) Modeling by : Gurhan Kayahan

Download or read book ARMA (Autoregressive-Moving Average) Modeling written by Gurhan Kayahan and published by . This book was released on 1988 with total page 89 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis estimates the frequency response of a network where the only data is the output obtained from an Autoregressive-moving average (ARMA) model driven by a random input. Models of random processes and existing methods for solving ARMA models are examined. The estimation is performed iteratively by using the Yule-Walker Equations in three different methods for the AR part and the Cholesky factorization for the MA part. The AR parameters are estimated initially, the MA parameters are estimated assuming that the AR parameters have been compensated for. After the estimation of each parameter set, the original time series is filtered via the inverse of the last estimate of the transfer function of an AR model or MA model, allowing better and better estimation of each model's coefficients. The iteration refers to the procedure of removing the MA or AR part from the random process in an alternating fashion allowing the creation of an almost pure AR or MA process, respectively. As the iteration continues the estimates are improving. When the iteration reaches a point where the coefficients converge the last MA and AR model coefficients are retained as final estimates. (kr).

Computer-based Short Term Forecasting for Job-shop Manufacturing Systems

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Publisher :
ISBN 13 :
Total Pages : 454 pages
Book Rating : 4.:/5 (89 download)

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Book Synopsis Computer-based Short Term Forecasting for Job-shop Manufacturing Systems by : Tsong-how Chang

Download or read book Computer-based Short Term Forecasting for Job-shop Manufacturing Systems written by Tsong-how Chang and published by . This book was released on 1972 with total page 454 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Time Series Analysis

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Publisher : Springer Science & Business Media
ISBN 13 : 0387759581
Total Pages : 501 pages
Book Rating : 4.3/5 (877 download)

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Book Synopsis Time Series Analysis by : Jonathan D. Cryer

Download or read book Time Series Analysis written by Jonathan D. Cryer and published by Springer Science & Business Media. This book was released on 2008-04-04 with total page 501 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents an accessible approach to understanding time series models and their applications. The ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment.