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 Shasha, Dennis Elliott.
 New York : Springer, c2004.
 Description
 Book — ix, 190 p. : ill. ; 24 cm.
 Summary

 I. Review of Techniques: Time series preliminaries Data reduction and transformation techniques Indexing methods Flexible similarity search. II. Case Studies: StatStream Query by humming Elastic burst detection A call to exploration. Answers to questions. References. Index.
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2. Time series analysis for social sciences [2014]
 BoxSteffensmeier, Janet M., 1965 author.
 New York, NY : Cambridge University Press, 2014.
 Description
 Book — xv, 280 pages : illustrations ; 23 cm.
 Summary

 Modeling social dynamics
 Univariate timeseries models
 Dynamic regression models
 Modeling the dynamics of social systems
 Univariate, nonstationary processes: tests and modeling
 Cointegration and error correction models
 Selections on time series analysis
 Concluding thoughts for the time series analyst.
 Online
 BoxSteffensmeier, Janet M., 1965 author.
 Cambridge : Cambridge University Press, 2014.
 Description
 Book — 1 online resource (xv, 280 pages) : digital, PDF file(s).
 Summary

 1. Modeling social dynamics
 2. Univariate time series models
 3. Dynamic regression models
 4. Modeling the dynamics of social systems
 5. Univariate, nonstationary processes: tests and modeling
 6. Cointegration and errorcorrection models
 7. Selections on time series analysis
 8. Concluding thoughts for the time series analyst.
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4. Journal of time series econometrics [2008  ]
 2008 : Berkeley, CA : Berkeley Electronic Press <2014> : Berlin, Germany : De Gruyter
 Description
 Journal/Periodical
 RamírezRojas, Alejandro.
 Amsterdam : Elsevier, 2019.
 Description
 Book — 1 online resource (406 p.)
 Summary

 1. Overview of open problems in seismology
 2. Stochastic processes
 3. Fractal time series
 4. Nonextensive statistics in time series: Tsallis theory.
 5. Natural time analysis
 6. Visibility graph analysis
 7. Multiscale analysis in time series
 8. Complexity measures
 9. Challenges in seismology.
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 Wei, William W. S., author.
 Second edition.  [Boston] : Pearson, [2019]
 Description
 Book — xxii, 614 pages ; 24 cm
 Online
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QA280 .W45 2019  Unknown 
 Singapore : Springer, 2017.
 Description
 Book — 1 online resource.
 Summary

 ""Preface""; ""Acknowledgements""; ""Contents""; ""1 Introduction""; ""1.1 On Empirical Causality""; ""1.2 Causality in Economic Analysis""; ""1.3 Empirical Economic Models ""; ""1.3.1 The Cowles Approach""; ""1.3.2 Economic TimeSeries Models""; ""1.4 Basic Concepts for Statistical Inference""; ""1.4.1 Conditional Inference""; ""1.4.2 Defining Exogeneity""; ""1.4.3 Interpretative Problems""; ""References""; ""2 The Measures of OneWay Effect, Reciprocity, and Association""; ""2.1 Prediction and Causality""; ""2.1.1 Statement of the Problem""; ""2.1.2 Terminology and Notations""
 ""2.2 Defining Noncausality""""2.3 The OneWay Effect Measure""; ""2.4 Alternative Methods for Deriving Mv tou(Î")""; ""2.4.1 DistributedLag Representation Approach""; ""2.4.2 Innovation Orthogonalization Approach""; ""2.5 Measures of Association and Reciprocity""; ""2.6 Examples""; ""References""; ""3 Representation of the Partial Measures""; ""3.1 Introduction""; ""3.2 ThirdSeries Involvement""; ""3.3 Partial Measures of Interdependence""; ""3.3.1 Representing the Partial Measures""; ""3.3.2 Glossary on Partial Measures of Interdependence""; ""3.3.3 The Stationary ARMA Model""
 ""3.4 Extension to Nonstationary Reproducible Processes""""References""; ""4 Inference Based on the Vector Autoregressive and Moving Average Model""; ""4.1 Inference Procedure""; ""4.1.1 ThreeStep Estimation Procedure""; ""4.1.2 Optimization Algorithm in Step 3""; ""4.1.3 Monte Carlo Wald Test of Measures of Interdependence""; ""4.1.4 Monte Carlo Wald Testing of Noncausality""; ""4.2 Simulation Performance""; ""4.2.1 Designing Monte Carlo Simulation""; ""4.2.2 Simulation Results""; ""4.2.3 Comparison of Step
 2 and Step
 3 Estimation""; ""4.3 Empirical Analysis of Macroeconomic Series""
 ""4.3.1 Literature""""4.3.2 Application of the Partial Measures to US Macroeconomic Data""; ""References""; ""5 Inference on Changes in Interdependence Measures""; ""5.1 Change in Measures""; ""5.1.1 Change in Measures for Stationary Vector ARMA Model""; ""5.1.2 Inference for Noncausal Relationship""; ""5.2 Tests Based on Subsampling Method""; ""5.2.1 Test for a Change in Measures Using HighFrequency Data""; ""5.2.2 Variance Estimation via Subsampling""; ""5.3 A Simulation Study of Finite Sample Test Properties""; ""5.3.1 Change in Simple Causality Measure""
 ""5.3.2 Change in Partial Causality Measure""""5.4 Empirical Illustrations""; ""5.4.1 Stock Returns and Dividend Yields""; ""5.4.2 IntraDaily Financial Time Series""; ""References""; ""Appendix Technical Supplements""
 Ryabko, Boris, author.
 Switzerland : Springer, 2016.
 Description
 Book — 1 online resource (ix, 145 pages) : illustrations (some color)
9. Time series analysis [2016]
 Palma, Wilfredo, 1963 author.
 Hoboken, New Jersey : John Wiley & Sons, Inc., [2016]
 Description
 Book — xxv, 579 pages : illustrations ; 24 cm.
 Summary

 Preface xiii
 Acknowledgments xvii
 Acronyms xix
 1 Introduction
 1
 1.1 Time Series Data
 2
 1.2 Random Variables and Statistical Modeling
 16
 1.3 DiscreteTime Models
 22
 1.4 Serial Dependence
 22
 1.5 Nonstationarity
 25
 1.6 Whiteness Testing
 32
 1.7 Parametric and Nonparametric Modeling
 36
 1.8 Forecasting
 38
 1.9 Time Series Modeling
 38
 1.10 Bibliographic Notes
 39
 Problems
 39
 2 Linear Processes
 43
 2.1 Definition
 44
 2.2 Stationarity
 44
 2.3 Invertibility
 45
 2.4 Causality
 46
 2.5 Representations of Linear Processes
 46
 2.6 Weak and Strong Dependence
 49
 2.7 ARMA Models
 51
 2.8 Autocovariance Function
 56
 2.9 ACF and Partial ACF Functions
 57
 2.10 ARFIMA Processes
 64
 2.11 Fractional Gaussian Noise
 71
 2.12 Bibliographic Notes
 72
 Problems
 72
 3 State Space Models
 89
 3.1 Introduction
 90
 3.2 Linear Dynamical Systems
 92
 3.3 State space Modeling of Linear Processes
 96
 3.4 State Estimation
 97
 3.5 Exogenous Variables
 113
 3.6 Bibliographic Notes
 114
 Problems
 114
 4 Spectral Analysis
 121
 4.1 Time and Frequency Domains
 122
 4.2 Linear Filters
 122
 4.3 Spectral Density
 123
 4.4 Periodogram
 125
 4.5 Smoothed Periodogram
 128
 4.6 Examples
 130
 4.7 Wavelets
 136
 4.8 Spectral Representation
 138
 4.9 TimeVarying Spectrum
 140
 4.10 Bibliographic Notes
 145
 Problems
 145
 5 Estimation Methods
 151
 5.1 Model Building
 152
 5.2 Parsimony
 152
 5.3 Akaike and Schwartz Information Criteria
 153
 5.4 Estimation of the Mean
 153
 5.5 Estimation of Autocovariances
 154
 5.6 Moment Estimation
 155
 5.7 MaximumLikelihood Estimation
 156
 5.8 Whittle Estimation
 157
 5.9 State Space Estimation
 160
 5.10 Estimation of LongMemory Processes
 161
 5.11 Numerical Experiments
 178
 5.12 Bayesian Estimation
 180
 5.13 Statistical Inference
 184
 5.14 Illustrations
 189
 5.15 Bibliographic Notes
 193
 Problems
 194
 6 Nonlinear Time Series
 209
 6.1 Introduction
 210
 6.2 Testing for Linearity
 211
 6.3 Heteroskedastic Data
 212
 6.4 ARCH Models
 213
 6.5 GARCH Models
 216
 6.6 ARFIMAGARCH Models
 218
 6.7 ARCH(1) Models
 220
 6.8 APARCH Models
 222
 6.9 Stochastic Volatility
 222
 6.10 Numerical Experiments
 223
 6.11 Data Applications
 225
 6.12 Value at Risk
 236
 6.13 Autocorrelation of Squares
 241
 6.14 Threshold autoregressive models
 247
 6.15 Bibliographic Notes
 252
 Problems
 253
 7 Prediction
 267
 7.1 Optimal Prediction
 268
 7.2 OneStep Ahead Predictors
 268
 7.3 Multistep Ahead Predictors
 275
 7.4 Heteroskedastic Models
 276
 7.5 Prediction Bands
 281
 7.6 Data Application
 287
 7.7 Bibliographic Notes
 289
 Problems
 289
 8 Nonstationary Processes
 295
 8.1 Introduction
 296
 8.2 Unit Root Testing
 297
 8.3 ARIMA Processes
 298
 8.4 Locally Stationary Processes
 301
 8.5 Structural Breaks
 326
 8.6 Bibliographic Notes
 331
 Problems
 332
 9 Seasonality
 337
 9.1 SARIMA Models
 338
 9.2 SARFIMA Models
 351
 9.3 GARMA Models
 353
 9.4 Calculation of the Asymptotic Variance
 355
 9.5 Autocovariance Function
 355
 9.6 Monte Carlo Studies
 359
 9.7 Illustration
 362
 9.8 Bibliographic Notes
 364
 Problems
 365
 10 Time Series Regression
 369
 10.1 Motivation
 370
 10.2 Definitions
 373
 10.3 Properties of the LSE
 375
 10.4 Properties of the BLUE
 376
 10.5 Estimation of the Mean
 379
 10.6 Polynomial Trend
 382
 10.7 Harmonic Regression
 386
 10.8 Illustration: Air Pollution Data
 388
 10.9 Bibliographic Notes
 392
 Problems
 392
 11 Missing Values and Outliers
 399
 11.1 Introduction
 400
 11.2 Likelihood Function with Missing Values
 401
 11.3 Effects of Missing Values on ML Estimates
 405
 11.4 Effects of Missing Values on Prediction
 407
 11.5 Interpolation of Missing Data
 410
 11.6 Spectral Estimation with Missing Values
 418
 11.7 Outliers and Intervention Analysis
 421
 11.8 Bibliographic Notes
 434
 Problems
 435
 12 NonGaussian Time Series
 441
 12.1 Data Driven Models
 442
 12.2 Parameter Driven Models
 452
 12.3 Estimation
 453
 12.4 Data Illustrations
 466
 12.5 ZeroInflated Models
 477
 12.6 Bibliographic Notes
 483
 Problems
 483
 Appendix A: Complements
 487
 A.1 Projection Theorem
 488
 A.2 Wold Decomposition
 490
 A.3 Bibliographic Notes
 497
 Appendix B: Solutions to Selected Problems
 499
 Appendix C: Data and Codes
 557
 References
 559
 Topic Index
 573
 Author Index 577.
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QA280 .P354 2016  Unknown 
 Switzerland : Springer, 2016.
 Description
 Book — 1 online resource (xix, 384 pages) : illustrations (some color).
 Oxford : Oxford University Press, 2016.
 Description
 Book — 1 online resource : illustrations (black and white)
 Summary

 1. Introduction
 2. The Development of a Time Series Methodology: from Recursive Residuals to Dynamic Conditional Score Models
 3. A StateDependent Model for Inflation Forecasting
 4. Measuring the Tracking Error of Exchange Traded Funds
 5. Measuring the Dynamics of Global Business Cycle Connectedness
 6. Inferring and Predicting Global Temperature Trends
 7. Forecasting the Boat Race
 8. Tests for Serial Dependence in Static, NonGaussian Factor Models
 9. Inference for Models with Asymmetric alphaStable Noise Processes
 10. Martingale Unobserved Component Models
 11. More is Not Always Better: Kalman Filtering in Dynamic Factor Models
 12. On Detecting EndofSample Instabilities
 13. Improved Frequentist Prediction Intervals for Autoregressive Models by Simulation
 14. The Superiority of the LM Test in a Class of Econometric Models Where the Wald Test Performs Poorly
 15. Generalised Linear Spectral Models.
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12. Applied econometric time series [2015]
 Enders, Walter, 1948 author.
 Fourth edition.  Hoboken, NJ : Wiley, [2015]
 Description
 Book — x, 485 pages : illustrations ; 23 cm
 Summary

 Difference equations
 Stationary timeseries models
 Modeling volatility
 Models with trend
 Multiequation timeseries models
 Cointegration and errorcorrection models
 Nonlinear models and breaks.
 Online
 Montgomery, Douglas C., author.
 Second edition.  Hoboken, N.J. : Wiley, 2015.
 Description
 Book — xiv, 643 pages : illustrations ; 24 cm.
 Summary

 PREFACE xi
 1 INTRODUCTION TO FORECASTING
 1 1.1 The Nature and Uses of Forecasts
 1 1.2 Some Examples of Time Series
 6 1.3 The Forecasting Process
 13 1.4 Data for Forecasting
 16 1.5 Resources for Forecasting
 19
 2 STATISTICS BACKGROUND FOR FORECASTING
 25 2.1 Introduction
 25 2.2 Graphical Displays
 26 2.3 Numerical Description of Time Series Data
 33 2.4 Use of Data Transformations and Adjustments
 46 2.5 General Approach to Time Series Modeling and Forecasting
 61 2.6 Evaluating and Monitoring Forecasting Model Performance
 64 2.7 R Commands for
 Chapter 2
 84
 3 REGRESSION ANALYSIS AND FORECASTING
 107 3.1 Introduction
 107 3.2 Least Squares Estimation in Linear Regression Models
 110 3.3 Statistical Inference in Linear Regression
 119 3.4 Prediction of New Observations
 134 3.5 Model Adequacy Checking
 136 3.6 Variable Selection Methods in Regression
 146 3.7 Generalized and Weighted Least Squares
 152 3.8 Regression Models for General Time Series Data
 177 3.9 Econometric Models
 205 3.10 R Commands for
 Chapter 3
 209
 4 EXPONENTIAL SMOOTHING METHODS
 233 4.1 Introduction
 233 4.2 FirstOrder Exponential Smoothing
 239 4.3 Modeling Time Series Data
 245 4.4 SecondOrder Exponential Smoothing
 247 4.5 HigherOrder Exponential Smoothing
 257 4.6 Forecasting
 259 4.7 Exponential Smoothing for Seasonal Data
 277 4.8 Exponential Smoothing of Biosurveillance Data
 286 4.9 Exponential Smoothers and Arima Models
 299 4.10 R Commands for
 Chapter 4
 300
 5 AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS
 327 5.1 Introduction
 327 5.2 Linear Models for Stationary Time Series
 328 5.2.1 Stationarity
 329 5.2.2 Stationary Time Series
 329 5.3 Finite Order Moving Average Processes
 333 5.4 Finite Order Autoregressive Processes
 337 5.5 Mixed Autoregressive Moving Average Processes
 354 5.6 Nonstationary Processes
 363 5.7 Time Series Model Building
 367 5.8 Forecasting Arima Processes
 378 5.9 Seasonal Processes
 383 5.10 Arima Modeling of Biosurveillance Data
 393 5.11 Final Comments
 399 5.12 R Commands for
 Chapter 5
 401
 6 TRANSFER FUNCTIONS AND INTERVENTION MODELS
 427 6.1 Introduction
 427 6.2 Transfer Function Models
 428 6.3 Transfer Function Noise Models
 436 6.4 CrossCorrelation Function
 436 6.5 Model Specification
 438 6.6 Forecasting with Transfer Function Noise Models
 456 6.7 Intervention Analysis
 462 6.8 R Commands for
 Chapter 6
 473
 7 SURVEY OF OTHER FORECASTING METHODS
 493 7.1 Multivariate Time Series Models and Forecasting
 493 7.3 Arch and Garch Models
 507 7.4 Direct Forecasting of Percentiles
 512 7.5 Combining Forecasts to Improve Prediction Performance
 518 7.6 Aggregation and Disaggregation of Forecasts
 522 7.7 Neural Networks and Forecasting
 526 7.8 Spectral Analysis
 529 7.9 Bayesian Methods in Forecasting
 535 7.10 Some Comments on Practical Implementation and Use of Statistical Forecasting Procedures
 542 7.11 R Commands for
 Chapter 7
 545 APPENDIX A STATISTICAL TABLES
 561 APPENDIX B DATA SETS FOR EXERCISES
 581 APPENDIX C INTRODUCTION TO R
 627 BIBLIOGRAPHY
 631 INDEX 639.
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QA280 .M662 2015  Unknown 
14. Applied time series analysis [2012]
 Woodward, Wayne A.
 Boca Raton, FL : CRC Press, c2012.
 Description
 Book — xxiii, 540 p. : ill. ; 25 cm.
 Summary

 Stationary Time Series Time Series Stationary Time Series Autocovariance and Autocorrelation Functions for Stationary Time Series Estimation of the Mean, Autocovariance, and Autocorrelation for Stationary Time Series Power Spectrum Estimating the Power Spectrum and Spectral Density for Discrete Time Series Time Series Examples Linear Filters Introduction to Linear Filters Stationary General Linear Processes Wold Decomposition Theorem Filtering Applications ARMA Time Series Models Moving Average Processes Autoregressive Processes AutoregressiveMoving Average Processes Visualizing Autoregressive Components Seasonal ARMA(p, q)x(Ps, Qs)s Models Generating Realizations from ARMA(p, q) Processes Transformations Other Stationary Time Series Models Stationary Harmonic Models ARCH and GARCH Models Nonstationary Time Series Models Deterministic SignalPlusNoise Models ARIMA(p, d, q) and ARUMA(p, d, q) Models Multiplicative Seasonal ARUMA(p, d, q) x (Ps, Ds, Qs)s Model Random Walk Models GStationary Models for Data with TimeVarying Frequencies Forecasting Mean Square Prediction Background BoxJenkins Forecasting for ARMA(p, q) Models Properties of the Best Forecast Xto(l) piWeight Form of the Forecast Function Forecasting Based on the Difference Equation Eventual Forecast Function Probability Limits for Forecasts Forecasts Using ARUMA(p, d, q) Models Forecasts Using Multiplicative Seasonal ARUMA Models Forecasts Based on SignalplusNoise Models Parameter Estimation Introduction Preliminary Estimates Maximum Likelihood Estimation of ARMA( p, q) Parameters Backcasting and Estimating sigma2a Asymptotic Properties of Estimators Estimation Examples Using Data ARMA Spectral Estimation ARUMA Spectral Estimation Model Identification Preliminary Check for White Noise Model Identification for Stationary ARMA Models Model Identification for Nonstationary ARUMA(p, d, q) Models Model Identification Based on Pattern Recognition Model Building Residual Analysis Stationarity versus Nonstationarity SignalplusNoise versus Purely AutocorrelationDriven Models Checking Realization Characteristics Comprehensive Analysis of Time Series Data: A Summary VectorValued (Multivariate) Time Series Multivariate Time Series Basics Stationary Multivariate Time Series Multivariate (Vector) ARMA Processes Nonstationary VARMA Processes Testing for Association between Time Series StateSpace Models Proof of Kalman Recursion for Prediction and Filtering LongMemory Processes Long Memory Fractional Difference and FARMA Models Gegenbauer and GARMA Processes kFactor Gegenbauer and GARMA Models Parameter Estimation and Model Identification Forecasting Based on the kFactor GARMA Model Modeling Atmospheric CO2 Data Using LongMemory Models Wavelets Shortcomings of Traditional Spectral Analysis for TVF Data Methods That Localize the "Spectrum" in Time Wavelet Analysis Wavelet Packets Concluding Remarks on Wavelets Appendix: Mathematical Preliminaries for This Chapter GStationary Processes GeneralizedStationary Processes MStationary Processes G(lambda)Stationary Processes Linear Chirp Processes Concluding Remarks Index.
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QA280 .W66 2012  Unknown 
 1st ed.  Amsterdam ; Boston : Elsevier/NorthHolland, 2012.
 Description
 Book — xviii, 755 p. : ill., maps ; 24 cm.
 Online
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QA280 .T53 2012  Unknown 
16. Análisis de series de tiempo [2011]
 Montenegro García, Alvaro, author.
 Primera edición.  Bogotá, D.C. : Pontificia Universidad Javeriana, abril de 2011.
 Description
 Book — 1 online resource : illustrations.
 Summary

 Conceptos y herramientas de análisis
 Modelo autorregresivo y modelo de promedio móvil
 Estimación de modelos ARMA (p, q)
 Modelos estacionarios multivariados
 Predicción económica
 Modelos ARCH
 Procesos estocásticos no estacionarios
 Raíces unitarias y cointegración bivariada
 Cointegración multivariada.
 Mills, Terence C.
 Houndmills, Basingstoke, Hampshire ; New York : Palgrave Macmillan, 2011.
 Description
 Book — xiv, 461 p. : ill. ; 24 cm.
 Summary

 Prolegomenon: A Personal Perspective and an Explanation of the Structure of the Book Yule and Hooker and the Concepts of Correlation and Trend Schuster, Beveridge and Periodogram Analysis Detrending and the Variate Difference Method: Student, Pearson and their Critics Nonsense Correlations, Random Shocks and Induced Cycles: Yule, Slutzky and Working Periodicities in Sunspots and Air Pressure: Yule, Walker and the Modelling of Superposed Fluctuations and Disturbances The Formal Modelling of Stationary Time Series: Wold and the Russians Generalizations and Extensions of Stationary Autoregressive Models: from Kendall to Box and Jenkins Statistical Inference, Estimation and Model Building for Stationary Time Series Dealing with Nonstationarity: Detrending, Smoothing and Differencing Forecasting Nonstationary Time Series Modelling Dynamic Relationships Between Time Series Spectral Analysis of Time Series: the Periodogram Revisited and Reclaimed Tacking Seasonal Patterns in Time Series Emerging Themes The Scene is Set References.
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 Varotsos, Panayiotis A.
 Berlin ; New York : Springer, c2011.
 Description
 Book — 1 online resource (xxiv, 449 p.) : maps (some col.)
 Online

 dx.doi.org SpringerLink
 Google Books (Full view)
 Bisgaard, Soren, 19512009
 Hoboken, N.J. : John Wiley & Sons, c2011.
 Description
 Book — xiii, 366 p. : ill. ; 25 cm.
 Summary

 Preface xi
 1. Time Series Data: Examples and Basic Concepts
 1 1.1 Introduction
 1 1.2 Examples of Time Series Data
 1 1.3 Understanding Autocorrelation
 10 1.4 The World Decomposition
 12 1.5 The Impulse Response Function
 14 1.6 Superposition Principle
 15 1.7 Parsimonious Models
 18 Exercises
 19
 2. Visualizing Time Series Data Structures: Graphical Tools
 21 2.1 Introduction
 21 2.2 Graphical Analysis of Time Series
 22 2.3 Graph Terminology
 23 2.4 Graphical Perception
 24 2.5 Principles of Graph Construction
 28 2.6 Aspect Ratio
 30 2.7 Time Series Plots
 34 2.8 Bad Graphics
 38 Exercises
 46
 3. Stationary Models
 47 3.1 Basics of Stationary Time Series Models
 47 3.2 Autoregressive Moving Average (ARMA) Models
 54 3.3 Stationary and Invertibility of ARMA Models
 62 3.4 Checking for Stationary using Variogram
 66 3.5 Transformation of Data
 69 Exercises
 73
 4. Nonstationary Models
 79 4.1 Introduction
 79 4.2 Detecting Nonstationarity
 79 4.3 Antoregressive Integrated Moving Average (ARIMA) Models
 83 4.4 Forecasting using ARIMA Models
 91 4.5 Example
 2: Concentration Measurements from a Chemical Process
 93 4.6 The EWMA Forecast
 103 Exercises
 104
 5. Seasonal Models
 111 5.1 Seasonal Data
 111 5.2 Seasonal ARIMA Models
 116 5.3 Forecasting using Seasonal ARIMA Models
 124 5.4 Example
 2: Company X's Sales Data
 126 Exercises
 152
 6. Time Series Model Selection
 155 6.1 Introduction
 155 6.2 Finding the "BEST" Model
 155 6.3 Example: Internet Users Data
 156 6.4 Model Selection Criteria
 163 6.5 Impulse Response Function to Study the Differences in Models
 166 6.6 Comparing Impulse Response Functions for Competing Models
 169 6.7 ARIMA Models as Rational Approximations
 170 6.8 AR Versus Arma Controversy
 171 6.9 Final Thoughts on Model Selection
 173 Appendix 6.1: How to Compute Impulse Response Functions with a Spreadsheet
 173 Exercises
 174
 7. Additional Issues in ARIMA Models
 177 7.1 Introduction
 177 7.2 Linear Difference Equations
 177 7.3 Eventual Forecast Function
 183 7.4 Deterministic Trend Models
 187 7.5 Yet Another Argument for Differencing
 189 7.6 Constant Term in ARIMA Models
 190 7.7 Cancellation of Terms in ARIMA Models
 191 7.8 Stochastic Trend: Unit Root Nonstationary Processes
 194 7.9 Overdifferencing and Underdifferencing
 195 7.10 Missing Values in Time Series Data
 197 Exercises
 201
 8. Transfer Function Models
 203 8.1 Introduction
 203 8.2 Studying InputOutput Relationships
 203 8.3 Example
 1: The BoxJenkins' Gas Furnace
 204 8.4 Spurious Cross Correlations
 207 8.5 Prewhitening
 207 8.6 Identification of the Transfer Function
 213 8.7 Modeling the Noise
 215 8.8 The General Methodology for Transfer Function Models
 222 8.9 Forecasting Using Transfer FunctionNoise Models
 224 8.10 Intervention Analysis
 238 Exercises
 261
 9. Addition Topics
 263 9.1 Spurious Relationships
 263 9.2 Autocorrelation in Regression
 271 9.3 Process Regime Changes
 278 9.4 Analysis of Multiple Time Series
 285 9.5 Structural Analysis of Multiple Time Series
 296 Exercises
 310 Appendix A. Datasets Used in the Examples
 311 Appendix B. Datasets Used in the Exercises
 327 Bibliography
 361 Index 365.
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QA280 .B575 2011  Unknown 
 Shumway, Robert H.
 3rd ed.  New York : Springer, c2011.
 Description
 Book — xi, 596 p. : ill. ; 24 cm.
 Summary

 Characteristics of time series. Time series regression and exploratory data analysis. ARIMA models. Spectral analysis and filtering. Additional time domain topics. Statespace models. Statistical methods in the frequency domain.
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QA280 .S585 2011  Unknown 
Articles+
Journal articles, ebooks, & other eresources
 Articles+ results include