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Forecasting: principles and practice

Forecasting: principles and practice
Author: Rob J Hyndman
Publisher: OTexts
Total Pages: 380
Release: 2018-05-08
Genre: Business & Economics
ISBN: 0987507117

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Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.


Forecasting, Structural Time Series Models and the Kalman Filter

Forecasting, Structural Time Series Models and the Kalman Filter
Author: Andrew C. Harvey
Publisher: Cambridge University Press
Total Pages: 574
Release: 1990
Genre: Business & Economics
ISBN: 9780521405737

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A synthesis of concepts and materials, that ordinarily appear separately in time series and econometrics literature, presents a comprehensive review of theoretical and applied concepts in modeling economic and social time series.


Time Series Models

Time Series Models
Author: Andrew C. Harvey
Publisher: Financial Times/Prentice Hall
Total Pages: 308
Release: 1993
Genre: Time-series analysis
ISBN: 9780745012001

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A companion volume to The Econometric Analysis of Time series, this book focuses on the estimation, testing and specification of dynamic models which are not based on any behavioural theory. It covers univariate and multivariate time series and emphasizes autoregressive moving-average processes.


Multiple Time Series Models

Multiple Time Series Models
Author: Patrick T. Brandt
Publisher: SAGE
Total Pages: 121
Release: 2007
Genre: Mathematics
ISBN: 1412906563

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Many analyses of time series data involve multiple, related variables. Modeling Multiple Time Series presents many specification choices and special challenges. This book reviews the main competing approaches to modeling multiple time series: simultaneous equations, ARIMA, error correction models, and vector autoregression. The text focuses on vector autoregression (VAR) models as a generalization of the other approaches mentioned. Specification, estimation, and inference using these models is discussed. The authors also review arguments for and against using multi-equation time series models. Two complete, worked examples show how VAR models can be employed. An appendix discusses software that can be used for multiple time series models and software code for replicating the examples is available. Key Features: * Offers a detailed comparison of different time series methods and approaches. * Includes a self-contained introduction to vector autoregression modeling. * Situates multiple time series modeling as a natural extension of commonly taught statistical models.


Time Series Models for Business and Economic Forecasting

Time Series Models for Business and Economic Forecasting
Author: Philip Hans Franses
Publisher: Cambridge University Press
Total Pages: 421
Release: 2014-04-24
Genre: Business & Economics
ISBN: 1139952129

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With a new author team contributing decades of practical experience, this fully updated and thoroughly classroom-tested second edition textbook prepares students and practitioners to create effective forecasting models and master the techniques of time series analysis. Taking a practical and example-driven approach, this textbook summarises the most critical decisions, techniques and steps involved in creating forecasting models for business and economics. Students are led through the process with an entirely new set of carefully developed theoretical and practical exercises. Chapters examine the key features of economic time series, univariate time series analysis, trends, seasonality, aberrant observations, conditional heteroskedasticity and ARCH models, non-linearity and multivariate time series, making this a complete practical guide. Downloadable datasets are available online.


Regression and Time Series Model Selection

Regression and Time Series Model Selection
Author: Allan D. R. McQuarrie
Publisher: World Scientific
Total Pages: 479
Release: 1998
Genre: Mathematics
ISBN: 9812385452

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This important book describes procedures for selecting a model from a large set of competing statistical models. It includes model selection techniques for univariate and multivariate regression models, univariate and multivariate autoregressive models, nonparametric (including wavelets) and semiparametric regression models, and quasi-likelihood and robust regression models. Information-based model selection criteria are discussed, and small sample and asymptotic properties are presented. The book also provides examples and large scale simulation studies comparing the performances of information-based model selection criteria, bootstrapping, and cross-validation selection methods over a wide range of models.


Multivariate Tests for Time Series Models

Multivariate Tests for Time Series Models
Author: Jeff B. Cromwell
Publisher: SAGE
Total Pages: 116
Release: 1994
Genre: Social sciences
ISBN: 9780803954403

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Which time series test should researchers choose to best describe the interactions among a set of time series variables? Providing guidelines for identifying the appropriate multivariate time series model to use, this book explores the nature and application of these increasingly complex tests.


Time Series

Time Series
Author: Raquel Prado
Publisher: CRC Press
Total Pages: 473
Release: 2021-07-27
Genre: Mathematics
ISBN: 1498747043

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• Expanded on aspects of core model theory and methodology. • Multiple new examples and exercises. • Detailed development of dynamic factor models. • Updated discussion and connections with recent and current research frontiers.


Bayesian Time Series Models

Bayesian Time Series Models
Author: David Barber
Publisher: Cambridge University Press
Total Pages: 432
Release: 2011-08-11
Genre: Computers
ISBN: 0521196760

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The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.


Threshold Models in Non-linear Time Series Analysis

Threshold Models in Non-linear Time Series Analysis
Author: H. Tong
Publisher: Springer Science & Business Media
Total Pages: 333
Release: 2012-12-06
Genre: Mathematics
ISBN: 1468478885

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In the last two years or so, I was most fortunate in being given opportunities of lecturing on a new methodology to a variety of audiences in Britain, China, Finland, France and Spain. Despite my almost Confucian attitude of preferring talking (i.e. a transient record) to writing (i.e. a permanent record), the warm encouragement of friends has led to the ensuing notes. I am also only too conscious of the infancy of the methodology introduced in these notes. However, it is my sincere hope that exposure to a wider audience will accelerate its maturity. Readers are assumed to be familiar with the basic theory of time series analysis. The book by Professor M.B. Priestley (1981) may be used as a general reference. Chapter One is addressed to the general question: "why do we need non-linear time series models?" After describing some significant advantages of linear models, it singles out several major limitations of linearity. Of course, the selection reflects my personal view on the subject, which is only at its very beginning, although there does seem to be a general agreement in the literature that time irr'eversibility and limit cycles are among the most obvious.