All free and available in most ereader formats.

Time Series Analysis [PDF Download]

Time Series Analysis PDF
Author: Tata Subba Rao
Publisher: Elsevier
Release Date: 2012
ISBN: 0444538585
Size: 61.36 MB
Format: PDF, ePub, Mobi
Category : Mathematics
Languages : en
Pages : 755
View: 980

Get Book

The field of statistics not only affects all areas of scientific activity, but also many other matters such as public policy. It is branching rapidly into so many different subjects that a series of handbooks is the only way of comprehensively presenting the various aspects of statistical methodology, applications, and recent developments. The Handbook of Statistics is a series of self-contained reference books. Each volume is devoted to a particular topic in statistics, with Volume 30 dealing with time series. The series is addressed to the entire community of statisticians and scientists in various disciplines who use statistical methodology in their work. At the same time, special emphasis is placed on applications-oriented techniques, with the applied statistician in mind as the primary audience. Comprehensively presents the various aspects of statistical methodology Discusses a wide variety of diverse applications and recent developments Contributors are internationally renowened experts in their respective areas

Time Series Analysis And Forecasting By Example [PDF Download]

Time Series Analysis and Forecasting by Example PDF
Author: Søren Bisgaard
Publisher: John Wiley & Sons
Release Date: 2011-08-24
ISBN: 9781118056950
Size: 22.95 MB
Format: PDF, ePub
Category : Mathematics
Languages : en
Pages : 400
View: 3118

Get Book

An intuition-based approach enables you to master time series analysis with ease Time Series Analysis and Forecasting by Example provides the fundamental techniques in time series analysis using various examples. By introducing necessary theory through examples that showcase the discussed topics, the authors successfully help readers develop an intuitive understanding of seemingly complicated time series models and their implications. The book presents methodologies for time series analysis in a simplified, example-based approach. Using graphics, the authors discuss each presented example in detail and explain the relevant theory while also focusing on the interpretation of results in data analysis. Following a discussion of why autocorrelation is often observed when data is collected in time, subsequent chapters explore related topics, including: Graphical tools in time series analysis Procedures for developing stationary, non-stationary, and seasonal models How to choose the best time series model Constant term and cancellation of terms in ARIMA models Forecasting using transfer function-noise models The final chapter is dedicated to key topics such as spurious relationships, autocorrelation in regression, and multiple time series. Throughout the book, real-world examples illustrate step-by-step procedures and instructions using statistical software packages such as SAS®, JMP, Minitab, SCA, and R. A related Web site features PowerPoint slides to accompany each chapter as well as the book's data sets. With its extensive use of graphics and examples to explain key concepts, Time Series Analysis and Forecasting by Example is an excellent book for courses on time series analysis at the upper-undergraduate and graduate levels. it also serves as a valuable resource for practitioners and researchers who carry out data and time series analysis in the fields of engineering, business, and economics.

An Introduction To State Space Time Series Analysis [PDF Download]

An Introduction to State Space Time Series Analysis PDF
Author: Jacques J. F. Commandeur
Publisher: OUP Oxford
Release Date: 2007-07-19
ISBN: 0191607800
Size: 27.92 MB
Format: PDF, ePub
Category : Business & Economics
Languages : en
Pages : 192
View: 5095

Get Book

Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader's knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time series on a daily basis in areas such as the social sciences, quantitative history, biology and medicine. It also serves as an accompanying textbook for a basic time series course in econometrics and statistics, typically at an advanced undergraduate level or graduate level.

Introduction To Time Series Analysis And Forecasting [PDF Download]

Introduction to Time Series Analysis and Forecasting PDF
Author: Douglas C. Montgomery
Publisher: John Wiley & Sons
Release Date: 2011-09-20
ISBN: 1118211502
Size: 17.96 MB
Format: PDF, Mobi
Category : Mathematics
Languages : en
Pages : 472
View: 6067

Get Book

An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences. As a result, there is a widespread need for large groups of people in a variety of fields to understand the basic concepts of time series analysis and forecasting. Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts, and it also bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts. Seven easy-to-follow chapters provide intuitive explanations and in-depth coverage of key forecasting topics, including: Regression-based methods, heuristic smoothing methods, and general time series models Basic statistical tools used in analyzing time series data Metrics for evaluating forecast errors and methods for evaluating and tracking forecasting performance over time Cross-section and time series regression data, least squares and maximum likelihood model fitting, model adequacy checking, prediction intervals, and weighted and generalized least squares Exponential smoothing techniques for time series with polynomial components and seasonal data Forecasting and prediction interval construction with a discussion on transfer function models as well as intervention modeling and analysis Multivariate time series problems, ARCH and GARCH models, and combinations of forecasts The ARIMA model approach with a discussion on how to identify and fit these models for non-seasonal and seasonal time series The intricate role of computer software in successful time series analysis is acknowledged with the use of Minitab, JMP, and SAS software applications, which illustrate how the methods are imple-mented in practice. An extensive FTP site is available for readers to obtain data sets, Microsoft Office PowerPoint slides, and selected answers to problems in the book. Requiring only a basic working knowledge of statistics and complete with exercises at the end of each chapter as well as examples from a wide array of fields, Introduction to Time Series Analysis and Forecasting is an ideal text for forecasting and time series courses at the advanced undergraduate and beginning graduate levels. The book also serves as an indispensable reference for practitioners in business, economics, engineering, statistics, mathematics, and the social, environmental, and life sciences.

Elements Of Multivariate Time Series Analysis [PDF Download]

Elements of Multivariate Time Series Analysis PDF
Author: Gregory C. Reinsel
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
ISBN: 146840198X
Size: 76.71 MB
Format: PDF, Docs
Category : Mathematics
Languages : en
Pages : 263
View: 2453

Get Book

The use of methods of time series analysis in the study of multivariate time series has become of increased interest in recent years. Although the methods are rather well developed and understood for univarjate time series analysis, the situation is not so complete for the multivariate case. This book is designed to introduce the basic concepts and methods that are useful in the analysis and modeling of multivariate time series, with illustrations of these basic ideas. The development includes both traditional topics such as autocovariance and auto correlation matrices of stationary processes, properties of vector ARMA models, forecasting ARMA processes, least squares and maximum likelihood estimation techniques for vector AR and ARMA models, and model checking diagnostics for residuals, as well as topics of more recent interest for vector ARMA models such as reduced rank structure, structural indices, scalar component models, canonical correlation analyses for vector time series, multivariate unit-root models and cointegration structure, and state-space models and Kalman filtering techniques and applications. This book concentrates on the time-domain analysis of multivariate time series, and the important subject of spectral analysis is not considered here. For that topic, the reader is referred to the excellent books by Jenkins and Watts (1968), Hannan (1970), Priestley (1981), and others.

Time Series Analysis [PDF Download]

Time Series Analysis PDF
Author: James Douglas Hamilton
Publisher: Taylor & Francis US
Release Date: 1994
ISBN: 9780691042893
Size: 70.90 MB
Format: PDF
Category : Business & Economics
Languages : en
Pages : 799
View: 3229

Get Book

In addition, Hamilton presents traditional tools for analyzing dynamic systems, including linear representations, autocovariance, generating functions, spectral analysis, and the Kalman filter, illustrating their usefulness both for economic theory and for studying and interpreting real-world data.

Time Series Analysis [PDF Download]

Time Series Analysis PDF
Author: Jonathan D. Cryer
Publisher: Springer Science & Business Media
Release Date: 2008-04-04
ISBN: 0387759581
Size: 33.78 MB
Format: PDF, ePub
Category : Business & Economics
Languages : en
Pages : 491
View: 5293

Get Book

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.

Regression Models For Time Series Analysis [PDF Download]

Regression Models for Time Series Analysis PDF
Author: Benjamin Kedem
Publisher: John Wiley & Sons
Release Date: 2005-03-11
ISBN: 0471461687
Size: 47.39 MB
Format: PDF, Docs
Category : Mathematics
Languages : en
Pages : 360
View: 1086

Get Book

A thorough review of the most current regression methods in timeseries analysis Regression methods have been an integral part of time seriesanalysis for over a century. Recently, new developments have mademajor strides in such areas as non-continuous data where a linearmodel is not appropriate. This book introduces the reader to newerdevelopments and more diverse regression models and methods fortime series analysis. Accessible to anyone who is familiar with the basic modern conceptsof statistical inference, Regression Models for Time SeriesAnalysis provides a much-needed examination of recent statisticaldevelopments. Primary among them is the important class of modelsknown as generalized linear models (GLM) which provides, under someconditions, a unified regression theory suitable for continuous,categorical, and count data. The authors extend GLM methodology systematically to time serieswhere the primary and covariate data are both random andstochastically dependent. They introduce readers to variousregression models developed during the last thirty years or so andsummarize classical and more recent results concerning state spacemodels. To conclude, they present a Bayesian approach to predictionand interpolation in spatial data adapted to time series that maybe short and/or observed irregularly. Real data applications andfurther results are presented throughout by means of chapterproblems and complements. Notably, the book covers: * Important recent developments in Kalman filtering, dynamic GLMs,and state-space modeling * Associated computational issues such as Markov chain, MonteCarlo, and the EM-algorithm * Prediction and interpolation * Stationary processes

Practical Time Series Analysis [PDF Download]

Practical Time Series Analysis PDF
Author: Aileen Nielsen
Publisher: O'Reilly Media
Release Date: 2019-09-20
ISBN: 1492041629
Size: 23.69 MB
Format: PDF, Docs
Category : Computers
Languages : en
Pages : 504
View: 6677

Get Book

Time series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase. Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly. You’ll get the guidance you need to confidently: Find and wrangle time series data Undertake exploratory time series data analysis Store temporal data Simulate time series data Generate and select features for a time series Measure error Forecast and classify time series with machine or deep learning Evaluate accuracy and performance

Time Series Analysis Univariate And Multivariate Methods [PDF Download]

Time Series Analysis Univariate and Multivariate Methods PDF
Author: William W. S. Wei
Publisher: Pearson
Release Date: 2018-03-14
ISBN: 9780134995366
Size: 61.78 MB
Format: PDF, Kindle
Category : Time-series analysis
Languages : en
Pages : 648
View: 155

Get Book

With its broad coverage of methodology, this comprehensive book is a useful learning and reference tool for those in applied sciences where analysis and research of time series is useful. Its plentiful examples show the operational details and purpose of a variety of univariate and multivariate time series methods. Numerous figures, tables and real-life time series data sets illustrate the models and methods useful for analyzing, modeling, and forecasting data collected sequentially in time. The text also offers a balanced treatment between theory and applications. Time Series Analysis is a thorough introduction to both time-domain and frequency-domain analyses of univariate and multivariate time series methods, with coverage of the most recently developed techniques in the field.