# Texts in Statistical Science The Analysis of Time Series An Introduction 6 Th Edition

₹785.00

- Publisher: CRC PRESS
- ISBN-13: 9781584883173
- Pages: 352
- Binding: Paperback
- Year of Pub / Reprint Year: 2017

## Description

**About The Book**

Provides wide-ranging, up-to-date coverage of both theory and practice

Offers a well-polished presentation, continually refined through five previous editions

Addresses practical problems and includes worked examples that help readers tackle the analysis of real data

Provides all of the data used in the book available for download at www.crcpress.com

Includes updated references to further reading

Summary

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.

**Table of Contents:**

INTRODUCTION

Some Representative Time Series

Terminology

Objectives of Time-Series Analysis

Approaches to Time-Series Analysis

Review of Books of Time Series

SIMPLE DESCRIPTIVE TECHNIQUES

Types of Variation

Stationary Time Series

The Time Plot

Transformation

Analysing Series that Contain a Trend

Analysing Series that Contain Seasonal Variation

Autocorrelation and the Correlogram

Other Tests of Randomness

Handling Real Data

PROBABILITY MODELS FOR TIME SERIES

Stochastic Processes and their Properties

Stationary Processes

Some Properties of the Autocorrelation Function

Some Useful Models

The Wold Decomposition Theorem

FITTING TIME-SERIES MODELS (IN THE TIME DOMAIN)

Estimating the Autocovariance and Autocorrelation Functions

Fitting an Autoregressive Process

Fitting a Moving Average Process

Estimating the Parameters of an ARMA Model

Estimating the Parameters of an ARIMA Model

The Box-Jenkins Seasonal (SARIMA) Model

Residual Analysis

General Remarks on Model Building

FORECASTING

Introduction

Univariate Procedures

Multivariate Procedures

A Comparative Review of Forecasting Procedures

Some Examples

Prediction Theory

STATIONARY PROCESSES IN THE FREQUENCY DOMAIN

Introduction

The Spectral Distribution Function

The Spectral Density Function

The Spectrum of a Continuous Process

Derivation of Selected Spectra

SPECTRAL ANALYSIS

Fourier Analysis

A Simple Sinusoidal Model

Periodogram Analysis

Spectral Analysis: some Consistent Estimation Procedures

Confidence Intervals for the Spectrum

A Comparison of Different Estimation Procedures

Analysing a Continuous Time Series

Examples and Discussion

BIVARIATE PROCESSES

The Cross-Covariance and Cross-Correlation Functions

The Cross-Spectrum

LINEAR SYSTEMS

Introduction

Linear systems in the Time Domain

Linear Systems in the Frequency Domain

Identification of Linear Systems

STATE-SPACE MODELS AND THE KALMAN FILTER

State-Space Models

The Kalman Filter

NON-LINEAR MODELS

Introduction

Some Models with Nonlinear Structure

Models for Changing Variance

Neural Networks

Chaos

Concluding Remarks

Bibliography

MULTIVARIATE TIME-SERIES MODELLING

Introduction

Single Equation Models

Vector Autoregressive Models

Vector ARMA Models

Fitting VAR and VARMA Models

Co-integration

Bibliography

SOME MORE ADVANCED TOPICS

Model Identification Tools

Modelling Non-Stationary Series

Fractional Differencing and Long-Memory Models

Testing for Unit Roots

The Effect of Model Uncertainty

Control Theory

Miscellanea

EXAMPLES AND PRACTICAL ADVICE

General Comments

Computer Software

Examples

More on the Time Plot

Concluding Remarks

Data Sources and Exercises

APPENDICES

The Fourier, Laplace, and z-Transforms

The Dirac Delta Function

Covariance and Correlation

Some MINITAB and S-PLUS Commands

ANSWERS TO EXERCISES

REFERENCES