The Analysis of Time Series An Introduction, Sixth Edition

"As an introduction to techniques for analyzing discrete time series, this textbook explains probability models, the spectral density function, time-invariant linear systems, state-space models, nonlinear models, and multivariate time series models."--"Book News, Inc.."

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.

INTRODUCTIONSome Representative Time Series TerminologyObjectives of Time-Series AnalysisApproaches to Time-Series AnalysisReview of Books of Time SeriesSIMPLE DESCRIPTIVE TECHNIQUESTypes of VariationStationary Time SeriesThe Time PlotTransformationAnalysing Series that Contain a TrendAnalysing Series that Contain Seasonal VariationAutocorrelation and the CorrelogramOther Tests of RandomnessHandling Real DataPROBABILITY MODELS FOR TIME SERIESStochastic Processes and their PropertiesStationary ProcessesSome Properties of the Autocorrelation FunctionSome Useful ModelsThe Wold Decomposition TheoremFITTING TIME-SERIES MODELS (IN THE TIME DOMAIN)Estimating the Autocovariance and Autocorrelation FunctionsFitting an Autoregressive ProcessFitting a Moving Average ProcessEstimating the Parameters of an ARMA ModelEstimating the Parameters of an ARIMA ModelThe Box-Jenkins Seasonal (SARIMA) ModelResidual AnalysisGeneral Remarks on Model BuildingFORECASTINGIntroductionUnivariate ProceduresMultivariate ProceduresA Comparative Review of Forecasting ProceduresSome ExamplesPrediction TheorySTATIONARY PROCESSES IN THE FREQUENCY DOMAINIntroductionThe Spectral Distribution FunctionThe Spectral Density FunctionThe Spectrum of a Continuous ProcessDerivation of Selected SpectraSPECTRAL ANALYSISFourier AnalysisA Simple Sinusoidal ModelPeriodogram AnalysisSpectral Analysis: some Consistent Estimation ProceduresConfidence Intervals for the SpectrumA Comparison of Different Estimation ProceduresAnalysing a Continuous Time SeriesExamples and DiscussionBIVARIATE PROCESSESThe Cross-Covariance and Cross-Correlation FunctionsThe Cross-SpectrumLINEAR SYSTEMSIntroductionLinear systems in the Time DomainLinear Systems in the Frequency DomainIdentification of Linear SystemsSTATE-SPACE MODELS AND THE KALMAN FILTERState-Space ModelsThe Kalman FilterNON-LINEAR MODELSIntroductionSome Models with Nonlinear StructureModels for Changing VarianceNeural NetworksChaosConcluding RemarksBibliographyMULTIVARIATE TIME-SERIES MODELLINGIntroductionSingle Equation ModelsVector Autoregressive ModelsVector ARMA ModelsFitting VAR and VARMA ModelsCo-integrationBibliographySOME MORE ADVANCED TOPICSModel Identification ToolsModelling Non-Stationary SeriesFractional Differencing and Long-Memory ModelsTesting for Unit RootsThe Effect of Model UncertaintyControl TheoryMiscellaneaEXAMPLES AND PRACTICAL ADVICEGeneral CommentsComputer SoftwareExamplesMore on the Time PlotConcluding RemarksData Sources and ExercisesAPPENDICESThe Fourier, Laplace, and z-TransformsThe Dirac Delta FunctionCovariance and CorrelationSome MINITAB and S-PLUS CommandsANSWERS TO EXERCISESREFERENCES