MARC ayrıntıları
| 000 -LEADER |
|---|
| fixed length control field |
11341nam a2200325 i 4500 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
110628s2012 lau b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2011025090 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781439818374 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Transcribing agency |
DLC |
| 049 ## - LOCAL HOLDINGS (OCLC) |
|---|
| Holding library |
BAUN_MERKEZ |
| 050 04 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA280 |
| Item number |
.W66 2012 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Edition number |
23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Woodward, Wayne A |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Applied time series analysis / |
| Statement of responsibility, etc |
Wayne A Woodward, Henry L. Gray, and Alan C. Elliott |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
|---|
| Place of production, publication, distribution, manufacture |
Boca Raton : |
| Name of producer, publisher, distributor, manufacturer |
Chapman and Hall/CRC, |
| Date of production, publication, distribution, manufacture, or copyright notice |
2012. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxiii, 540 pages : |
| Other physical details |
illustrations, |
| Dimensions |
25 cm |
| 336 ## - CONTENT TYPE |
|---|
| Content Type Term |
text |
| Content Type Code |
txt |
| Source |
rdacontent |
| 337 ## - MEDIA TYPE |
|---|
| Media Type Term |
unmediated |
| Media Type Code |
unmediated |
| Source |
rdamedia |
| 338 ## - CARRIER TYPE |
|---|
| Carrier Type Term |
volume |
| Carrier Type Code |
volume |
| Source |
rdacarrier |
| 490 0# - SERIES STATEMENT |
|---|
| Series statement |
Statistics: textbooks and monographs |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index |
| 505 00 - FORMATTED CONTENTS NOTE |
|---|
| Title |
Contents |
| -- |
Preface |
| -- |
Acknowledgments |
| -- |
1. Stationary Time Series |
| -- |
1.1. Time Series |
| -- |
1.2. Stationary Time Series |
| -- |
1.3. Autocovariance and Autocorrelation Functions for Stationary Time Series |
| -- |
1.4. Estimation of the Mean, Autocovariance, and Autocorrelation for Stationary Time Series |
| -- |
1.4.1. Estimation of μ |
| -- |
1.4.1.1. Ergodicity of X |
| -- |
1.4.1.2. Variance of X |
| -- |
1.4.2. Estimation of γk |
| -- |
1.4.3. Estimation of ρk |
| -- |
1.5. Power Spectrum |
| -- |
1.6. Estimating the Power Spectrum and Spectral Density for Discrete Time Series |
| -- |
1.7. Time Series Examples |
| -- |
1.7.1. Simulated Data |
| -- |
1.7.2. Real Data |
| -- |
1.A. Appendix |
| -- |
Exercises |
| -- |
2. Linear Filters |
| -- |
2.1. Introduction to Linear Filters |
| -- |
2.1.1. Relationship between the Spectra of the Input and Output of a Linear Filter |
| -- |
2.2. Stationary General Linear Processes |
| -- |
2.2.1. Spectrum and Spectral Density for a General Linear Process |
| -- |
2.3. Wold Decomposition Theorem |
| -- |
2.4. Filtering Applications |
| -- |
2.4.1. Butterworth Filters |
| -- |
2.A. Appendix |
| -- |
Exercises |
| -- |
3. ARMA Time Series Models |
| -- |
3.1. Moving Average Processes |
| -- |
3.1.1. MA(1) Model |
| -- |
3.1.2. MA(2) Model |
| -- |
3.2. Autoregressive Processes |
| -- |
3.2.1. Inverting the Operator |
| -- |
3.2.2. AR(1) Model |
| -- |
3.2.3. AR(p) Model for p [≥] 1 |
| -- |
3.2.4. Autocorrelations of an AR(p) Model |
| -- |
3.2.5. Linear Difference Equations |
| -- |
3.2.6. Spectral Density of an AR(p) Model |
| -- |
3.2.7. AR(2) Model |
| -- |
3.2.7.1. Autocorrelations of an AR(2) Model |
| -- |
3.2.7.2. Spectral Density of an AR(2) |
| -- |
3.2.7.3. Stationary/Causal Region of an AR(2) |
| -- |
3.2.7.4. ψ-Weights of an AR(2) Model |
| -- |
3.2.8. Summary of AR(1) and AR(2) Behavior |
| -- |
3.2.9. AR(p) Model |
| -- |
3.2.10. AR(1) and AR(2) Building Blocks of an AR(p) Model |
| -- |
3.2.11. Factor Tables |
| -- |
3.2.12. Invertibility/Infinite-Order Autoregressive Processes |
| -- |
3.2.13. Two Reasons for Imposing Invertibility |
| -- |
3.3. Autoregressive-Moving Average Processes |
| -- |
3.3.1. Stationarity and Invertibility Conditions for an ARMA(p,q) Model |
| -- |
3.3.2. Spectral Density of an ARMA(p,q) Model |
| -- |
3.3.3. Factor Tables and ARMA(p,q) Models |
| -- |
3.3.4. Autocorrelations of an ARMA(p,q) Model |
| -- |
3.3.5. ψ-Weights of an ARMA(p,q) |
| -- |
3.3.6. Approximating ARMA(p,q) Processes Using High-Order AR(p) Models |
| -- |
3.4. Visualizing Autoregressive Components |
| -- |
3.5. Seasonal ARMA(p,q) x (PsrQs)s Models |
| -- |
3.6. Generating Realizations from ARMA(p,q) Processes |
| -- |
3.6.1. MA(q) Model |
| -- |
3.6.2. AR(2) Model |
| -- |
3.6.3. General Procedure |
| -- |
3.7. Transformations |
| -- |
3.7.1. Memoryless Transformations |
| -- |
3.7.2. Autoregressive Transformations |
| -- |
3.A. Appendix: Proofs of Theorems |
| -- |
Exercises |
| -- |
4. Other Stationary Time Series Models |
| -- |
4.1. Stationary Harmonic Models |
| -- |
4.1.1. Pure Harmonic Models |
| -- |
4.1.2. Harmonic Signal-plus-Noise Models |
| -- |
4.1.3. ARMA Approximation to the Harmonic Signal-plus-Noise Model |
| -- |
4.2. ARCH and GARCH Processes |
| -- |
4.2.1. ARCH Processes |
| -- |
4.2.1.1. The ARCH(1) Model |
| -- |
4.2.1.2. The ARCH(90) Model |
| -- |
4.2.2. The GARCH(po,qo) Process |
| -- |
4.2.3. AR Processes with ARCH or GARCH Noise |
| -- |
Exercises |
| -- |
5. Nonstationary Time Series Models |
| -- |
5.1. Deterministic Signal-plus-Noise Models |
| -- |
5.1.1. Trend-Component Models |
| -- |
5.1.2. Harmonic Component Models |
| -- |
5.2. ARIMA(p,d,q) and ARUMA(p,d,q) Processes |
| -- |
5.2.1. Extended Autocorrelations of an ARUMA(p,d,q) Process |
| -- |
5.2.2. Cyclical Models |
| -- |
5.3. Multiplicative Seasonal ARUMA(p,d,q) x (PsrDsrQs)s Process |
| -- |
5.3.1. Factor Tables for Seasonal Models of the Form (5.17) with s = 4 and s = 12 |
| -- |
5.4. Random Walk Models |
| -- |
5.4.1. Random Walk |
| -- |
5.4.2. Random Walk with Drift |
| -- |
5.5. G-Stationary Models for Data with Time-Varying Frequencies |
| -- |
Exercises |
| -- |
6. Forecasting |
| -- |
6.1. Mean Square Prediction Background |
| -- |
6.2. Box-Jenkins Forecasting for ARMA(p,q) Models |
| -- |
6.3. Properties of the Best Forecast Zto(l) |
| -- |
6.4. π-Weight Form of the Forecast Function |
| -- |
6.5. Forecasting Based on the Difference Equation |
| -- |
6.6. Eventual Forecast Function |
| -- |
6.7. Probability Limits for Forecasts |
| -- |
6.8. Forecasts Using ARUMA(p,d,q) Models |
| -- |
6.9. Forecasts Using Multiplicative Seasonal ARUMA Models |
| -- |
6.10. Forecasts Based on Signal-plus-Noise Models |
| -- |
6.A. Appendix |
| -- |
Exercises |
| -- |
7. Parameter Estimation |
| -- |
7.1. Introduction |
| -- |
7.2. Preliminary Estimates |
| -- |
7.2.1. Preliminary Estimates for AR(p) Models |
| -- |
7.2.1.1. Yule-Walker Estimates |
| -- |
7.2.1.2. Least Squares Estimation |
| -- |
7.2.1.3. Burg Estimates |
| -- |
7.2.2. Preliminary Estimates for MA(q) Models |
| -- |
7.2.2.1. Method-of-Moment Estimation for an MA(q) |
| -- |
7.2.2.2. MA(q) Estimation Using the Innovations Algorithm |
| -- |
7.2.3. Preliminary Estimates for ARMA(p,q) Models |
| -- |
7.2.3.1. Extended Yule-Walker Estimates of the Autoregressive Parameters |
| -- |
7.2.3.2. Tsay-Tiao (TT) Estimates of the Autoregressive Parameters |
| -- |
7.2.3.3. Estimating the Moving Average Parameters |
| -- |
7.3. Maximum Likelihood Estimation of ARMA(p,q) Parameters |
| -- |
7.3.1. Conditional and Unconditional Maximum Likelihood Estimation |
| -- |
7.3.2. ML Estimation Using the Innovations Algorithm |
| -- |
7.4. Backcasting and Estimating σ2a |
| -- |
7.5. Asymptotic Properties of Estimators |
| -- |
7.5.1. Autoregressive Case |
| -- |
7.5.1.1. Confidence Intervals: Autoregressive Case |
| -- |
7.5.2. ARMA(p,q) Case |
| -- |
7.5.2.1. Confidence Intervals for ARMA(p,q) Parameters |
| -- |
7.5.3. Asymptotic Comparisons of Estimators for an MA(1) |
| -- |
7.6. Estimation Examples Using Data |
| -- |
7.7. ARMA Spectral Estimation |
| -- |
7.8. ARUMA Spectral Estimation |
| -- |
Exercises |
| -- |
8. Model Identification |
| -- |
8.1. Preliminary Check for White Noise |
| -- |
8.2. Model Identification for Stationary ARMA Models |
| -- |
8.2.1. Model Identification Based on AIC and Related Measures |
| -- |
8.3. Model Identification for Nonstationary ARUMA(p,d,q) Models |
| -- |
8.3.1. Including a Nonstationary Factor in the Model |
| -- |
8.3.2. Identifying Nonstationary Component(s) in a Model |
| -- |
8.3.3. Decision between a Stationary or a Nonstationary Model |
| -- |
8.3.4. Deriving a Final ARUMA Model |
| -- |
8.3.5. More on the Identification of Nonstationary Components |
| -- |
8.3.5.1. Including a Factor (1 - B)d in the Model |
| -- |
8.3.5.2. Testing for a Unit Root |
| -- |
8.3.5.3. Including a Seasonal Factor (1 - Bs) in the Model |
| -- |
8.A. Appendix: Model Identification Based on Pattern Recognition |
| -- |
Exercises |
| -- |
9. Model Building |
| -- |
9.1. Residual Analysis |
| -- |
9.1.1. Check Sample Autocorrelations of Residuals versus 95% Limit Lines |
| -- |
9.1.2. Ljung-Box Test |
| -- |
9.1.3. Other Tests for Randomness |
| -- |
9.1.4. Testing Residuals for Normality |
| -- |
9.2. Stationarity versus Nonstationarity |
| -- |
9.3. Signal-plus-Noise versus Purely Autocorrelation-Driven Models |
| -- |
9.3.1. Cochrane Orcutt, ML, and Frequency Domain Method |
| -- |
9.3.2. A Bootstrapping Approach |
| -- |
9.3.3. Other Methods for Trend Testing |
| -- |
9.4. Checking Realization Characteristics |
| -- |
9.5. Comprehensive Analysis of Time Series Data: A Summary |
| -- |
Exercises |
| -- |
10. Vector-Valued (Multivariate) Time Series |
| -- |
10.1. Multivariate Time Series Basics |
| -- |
10.2. Stationary Multivariate Time Series |
| -- |
10.2.1. Estimating the Mean and Covariance for Stationary Multivariate Processes |
| -- |
10.2.1.1. Estimating μ |
| -- |
10.2.1.2. Estimating π(k) |
| -- |
10.3. Multivariate (Vector) ARMA Processes |
| -- |
10.3.1. Forecasting Using VAR(p) Models |
| -- |
10.3.2. Spectrum of a VAR(p) Model |
| -- |
10.3.3. Estimating the Coefficients of a VAR(p) Model |
| -- |
10.3.3.1. Yule-Walker Estimation |
| -- |
10.3.3.2. Least Squares and Conditional Maximum Likelihood Estimation |
| -- |
10.3.3.3. Burg-Type Estimation |
| -- |
10.3.4. Calculating the Residuals and Estimating πa |
| -- |
10.3.5. VAR(p) Spectral Density Estimation |
| -- |
10.3.6. Fitting a VAR(p) Model to Data |
| -- |
10.3.6.1. Model Selection |
| -- |
10.3.6.2. Estimating the Parameters |
| -- |
10.3.6.3. Testing the Residuals for White Noise |
| -- |
10.4. Nonstationary VARMA Processes |
| -- |
10.5. Testing for Association between Time Series |
| -- |
10.5.1. Testing for Independence of Two Stationary Time Series |
| -- |
10.5.2. Testing for Cointegration between Nonstationary Time Series |
| -- |
10.6. State-Space Models |
| -- |
10.6.1. State Equation |
| -- |
10.6.2. Observation Equation |
| -- |
10.6.3. Goals of State-Space Modeling |
| -- |
10.6.4. Kalman Filter |
| -- |
10.6.4.1. Prediction (Forecasting) |
| -- |
10.6.4.2. Filtering |
| -- |
10.6.4.3. Smoothing Using the Kalman |
|
|---|
| Title |
Filter |
| -- |
10.6.4.4. H-Step Ahead Predictions |
| -- |
10.6.5. Kalman Filter and Missing Data |
| -- |
10.6.6. Parameter Estimation |
| -- |
10.6.7. Using State-Space Methods to Find Additive Components of a Univariate Autoregressive Realization |
| -- |
10.6.7.1. Revised State-Space Model |
| -- |
10.6.7.2. ψ Real |
| -- |
10.6.7.3. ψ Complex |
| -- |
10.A. Appendix: Derivation of State-Space Results |
| -- |
Exercises |
| -- |
11. Long-Memory Processes |
| -- |
11.1. Long Memory |
| -- |
11.2. Fractional Difference and FARMA Processes |
| -- |
11.3. Gegenbauer and GARMA Processes |
| -- |
11.3.1. Gegenbauer Polynomials |
| -- |
11.3.2. Gegenbauer Process |
| -- |
11.3.3. GARMA Process |
| -- |
11.4. K-Factor Gegenbauer And Garma Processes |
| -- |
11.4.1. Calculating Autocovariances |
| -- |
11.4.2. Generating Realizations |
| -- |
11.5. Parameter Estimation and Model Identification |
| -- |
11.6. Forecasting Based on the k-Factor GARMA Model |
| -- |
11.7. Modeling Atmospheric CO2 Data Using Long-Memory Models |
| -- |
Exercises |
| -- |
12. Wavelets |
| -- |
12.1. Shortcomings of Traditional Spectral Analysis for TVF Data |
| -- |
12.2. Window-Based Methods That Localize the "Spectrum" in Time |
| -- |
12.2.1. Gabor Spectrogram |
| -- |
12.2.2. Wigner-Ville Spectrum |
| -- |
12.3. Wavelet Analysis |
| -- |
12.3.1. Fourier Series Background |
| -- |
12.3.2. Wavelet Analysis Introduction |
| -- |
12.3.3. Fundamental Wavelet Approximation Result |
| -- |
12.3.4. Discrete Wavelet Transform for Data Sets of Finite Length |
| -- |
12.3.5. Pyramid Algorithm |
| -- |
12.3.6. Multiresolution Analysis |
| -- |
12.3.7. Wavelet Shrinkage |
| -- |
12.3.8. Scalogram: Time-Scale Plot |
| -- |
12.3.9. Wavelet Packets |
| -- |
12.3.10. Two-Dimensional Wavelets |
| -- |
12.5. Concluding Remarks on Wavelets |
| -- |
12.A. Appendix: Mathematical Preliminaries for This Chapter |
| -- |
Exercises |
| -- |
13. G-Stationary Processes |
| -- |
13.1. Generalized-Stationary Processes |
| -- |
13.1.1. General Strategy for Analyzing G-Stationary Processes |
| -- |
13.2. M-Stationary Processes |
| -- |
13.2.1. Continuous M-Stationary Process |
| -- |
13.2.2. Discrete M-Stationary Process |
| -- |
13.2.3. Discrete Euler(p) Model |
| -- |
13.2.4. Time Transformation and Sampling |
| -- |
13.3. G(λ)-Stationary Processes |
| -- |
13.3.1. Continuous G(p;λ) Model |
| -- |
13.3.2. Sampling the Continuous G(λ)-Stationary Processes |
| -- |
13.3.2.1. Equally Spaced Sampling from G(p;λ) Processes |
| -- |
13.3.3. Analyzing TVF Data Using the G(p;λ) Model |
| -- |
13.3.3.1. G(p;λ) Spectral Density |
| -- |
13.4. Linear Chirp Processes |
| -- |
13.4.1. Models for Generalized Linear Chirps |
| -- |
13.5. Concluding Remarks |
| -- |
13.A. Appendix |
| -- |
Exercises |
| -- |
References |
| -- |
Index |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Time-series analysis |
| 700 1# - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Gray, Henry L |
|
|---|
| Personal name |
Elliott, Alan C., |
| Dates associated with a name |
1952- |
| 900 ## - EQUIVALENCE OR CROSS-REFERENCE-PERSONAL NAME [LOCAL, CANADA] |
|---|
| Personal Name |
34755 |
|
|---|
| Numeration |
satın |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Kitap |
-baunlogo.png?alt=media&token=2b1f50b7-298a-48ee-a2b1-6fcf8e70b387)
-Facebook-2024.png?alt=media&token=2fcc0655-0091-431c-8ee0-059464d6eda1){kind=small}
-Instagram-2024.png?alt=media&token=a143a92a-6f9d-45a7-9ba2-9cc62f92e772){kind=small}