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The Analysis Of Time Series

Author : Chris Chatfield
ISBN : 0203491688
Genre : Mathematics
File Size : 57.30 MB
Format : PDF, ePub, Mobi
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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 A free online appendix on time series analysis using R can be accessed at 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.
Category: Mathematics

The Analysis Of Time Series

Author : Christopher Chatfield
ISBN : UOM:39015016364666
Genre : Série chronologique
File Size : 25.13 MB
Format : PDF, Docs
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Simple descriptive techniques; Probability models for series; Estimation in the time domain; Forecasting; Stationary processes in the frequency domain; Spectral analysis; Bivariate processes; Linear systems.
Category: Série chronologique

Introduction To Time Series Analysis And Forecasting

Author : Douglas C. Montgomery
ISBN : 9781118211502
Genre : Mathematics
File Size : 87.38 MB
Format : PDF, ePub
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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.
Category: Mathematics

The Analysis Of Time Series

Author : Christopher Chatfield
ISBN : UVA:X000275229
Genre : Série chronologique
File Size : 23.58 MB
Format : PDF, Docs
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Category: Série chronologique

An Introduction To State Space Time Series Analysis

Author : Jacques J. F. Commandeur
ISBN : 9780191607806
Genre : Business & Economics
File Size : 37.80 MB
Format : PDF, ePub
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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.
Category: Business & Economics

Design And Analysis Of Time Series Experiments

Author : Richard McCleary
ISBN : 9780190661564
Genre : Medical
File Size : 70.92 MB
Format : PDF
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Design and Analysis of Time Series Experiments presents the elements of statistical time series analysis while also addressing recent developments in research design and causal modeling. A distinguishing feature of the book is its integration of design and analysis of time series experiments.Drawing examples from criminology, economics, education, pharmacology, public policy, program evaluation, public health, and psychology, Design and Analysis of Time Series Experiments is addressed to researchers and graduate students in a wide range of behavioral, biomedical and social sciences.Readers learn not only how-to skills but, also the underlying rationales for the design features and the analytical methods. ARIMA algebra, Box-Jenkins-Tiao models and model-building strategies, forecasting, and Box-Tiao impact models are developed in separate chapters. The presentation of themodels and model-building assumes only exposure to an introductory statistics course, with more difficult mathematical material relegated to appendices. Separate chapters cover threats to statistical conclusion validity, internal validity, construct validity, and external validity with an emphasison how these threats arise in time series experiments. Design structures for controlling the threats are presented and illustrated through examples. The chapters on statistical conclusion validity and internal validity introduce Bayesian methods, counterfactual causality and synthetic control groupdesigns. Building on the earlier of the authors, Design and Analysis of Time Series Experiments includes more recent developments in modeling, and considers design issues in greater detail than any existing work. Additionally, the book appeals to those who want to conduct or interpret time seriesexperiments, as well as to those interested in research designs for causal inference.
Category: Medical