Time series analysis: univariate and multivariate methods
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| Main Author: | |
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| Format: | Book |
| Language: | English |
| Published: |
Boston [u.a.]
Pearson Addison Wesley
2006
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| Edition: | 2. ed. |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXII, 614 S. graph. Darst. |
| ISBN: | 0321322169 |
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MARC
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| 100 | 1 | |a Wei, William W. S. |d 1940- |e Verfasser |0 (DE-588)17014495X |4 aut | |
| 245 | 1 | 0 | |a Time series analysis |b univariate and multivariate methods |c William W. S. Wei |
| 250 | |a 2. ed. | ||
| 264 | 1 | |a Boston [u.a.] |b Pearson Addison Wesley |c 2006 | |
| 300 | |a XXII, 614 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
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| 650 | 7 | |a Análise de séries temporais |2 larpcal | |
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| 650 | 4 | |a aTime-series analysis | |
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Record in the Search Index
| _version_ | 1804133612090556416 |
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| adam_text | TIME SERIES ANALYSIS UNIVARIATE AND MULTIVARIATE METHODS SECOND EDITION
WILLIAM W. S. WEI DEPARTMENT OF STATISTICS THE FOX SCHOOL OF BUSINESS
AND MANAGEMENT TEMPLE UNIVERSITY PEARSON ADDISON WESLEY BOSTON SAN
FRANCISCO NEW YORK LONDON TORONTO SYDNEY TOKYO SINGAPORE MADRID MEXICO
CITY MUNICH PARIS CAPE TOWN HONG KONG MONTREAL CONTENTS CHAPTER 1
CHAPTER 2 CHAPTER 3 PREFACE OVERVIEW 1.1 INTRODUCTION 1.2 EXAMPLES AND
SCOPE OF THIS BOOK FUNDAMENTAL CONCEPTS 2.1 STOCHASTIC PROCESSES 2.2 THE
AUTOCOVARIANCE AND AUTOCORRELATION FUNCTIONS 2.3 THE PARTIAL
AUTOCORRELATION FUNCTION 2.4 WHITE NOISE PROCESSES 2.5 ESTIMATION OF THE
MEAN, AUTOCOVARIANCES, AND AUTOCORRELATIONS 2.5.1 SAMPLE MEAN 2.5.2
SAMPLE AUTOCOVARIANCE FUNCTION 2.5.3 SAMPLE AUTOCORRELATION FUNCTION
2.5.4 SAMPLE PARTIAL AUTOCORRELATION FUNCTION 2.6 MOVING AVERAGE AND
AUTOREGRESSIVE REPRESENTATIONS OF 2.7 TIME SERIES PROCESSES LINEAR
DIFFERENCE EQUATIONS EXERCISES STATIONARY TIME SERIES MODELS 3.1
AUTOREGRESSIVE PROCESSES 3.1.1 THE FIRST-ORDER AULOREGRESSIVE AR( 1)
PROCESS 3.1.2 THE SECOND-ORDER AUTOREGRESSIVE AR(2) PROCESS 3.1.3 THE
GENERAL PTH-ORDER AUTOREGRESSIVE AR(P) PROCESS 3.2 MOVING AVERAGE
PROCESSES 3.2.1 THE FIRST-ORDER MOVING AVERAGE MA( 1) PROCESS 3.2.2 THE
SECOND-ORDER MOVING AVERAGE MA(2) PROCESS 3.2.3 THE GENERAL GTH-ORDER
MOVING AVERAGE MA( XIX 1 1 1 6 6 10 11 15 16 17 18 20 22 23 26 30 33 33
34 39 45 47 48 51 52 IX CONTENTS 3.3 THE DUAL RELATIONSHIP BETWEEN AR(P)
AND MA(IJ) PROCESSES 3.4 AUTOREGRESSIVE MOVING AVERAGE ARMA(P, Q)
PROCESSES 3.4.1 THE GENERAL MIXED ARMA(P, Q) PROCESS 3.4.2 THE ARMA( 1,
1) PROCESS EXERCISES 54 57 57 59 66 CHAPTER 4 NONSTATIONARY TIME SERIES
MODELS 68 4.1 NONSTATIONARITY IN THE MEAN 69 4.1.1 DETERMINISTIC TREND
MODELS 69 4.1.2 STOCHASTIC TREND MODELS AND DIFFERENCING 70 4.2
AUTOREGRESSIVE INTEGRATED MOVING AVERAGE (ARIMA) MODELS 71 4.2.1 THE
GENERAL ARIMA MODEL 72 4.2.2 THE RANDOM WALK MODEL 72 4.2.3 THE ARIMA(0,
1, 1) OR IMA( 1,1) MODEL 74 4.3 NONSTATIONARITY IN THE VARIANCE AND THE
AUTOCOVARIANCE 80 4.3.1 VARIANCE AND AUTOCOVARIANCE OF THE ARIMA MODELS
82 4.3.2 VARIANCE STABILIZING TRANSFORMATIONS 83 EXERCISES 86 CHAPTER 5
FORECASTING 5.1 INTRODUCTION 5.2 MINIMUM MEAN SQUARE ERROR FORECASTS
5.2.1 MINIMUM MEAN SQUARE ERROR FORECASTS FOR ARMA MODELS 5.2.2 MINIMUM
MEAN SQUARE ERROR FORECASTS FOR ARIMA MODELS 5.3 COMPUTATION OF
FORECASTS 5.4 THE ARIMA FORECAST AS A WEIGHTED AVERAGE OF PREVIOUS
OBSERVATIONS 5.5 UPDATING FORECASTS 5.6 EVENTUAL FORECAST FUNCTIONS 5.7
A NUMERICAL EXAMPLE EXERCISES 88 88 88 89 91 94 96 99 100 103 105
CHAPTER 6 MODEL IDENTIFICATION 6.1 STEPS FOR MODEL IDENTIFICATION 6.2
EMPIRICAL EXAMPLES 6.3 THE INVERSE AUTOCORRELATION FUNCTION (IACF) 108
108 111 122 CONTENTS XI 6.4 EXTENDED SAMPLE AUTOCORRELATION FUNCTION AND
OTHER IDENTIFICATION PROCEDURES 128 6.4.1 THE EXTENDED SAMPLE
AUTOCORRELATION FUNCTION (ESACF) 128 6.4.2 OTHER IDENTIFICATION
PROCEDURES 133 EXERCISES 134 CHAPTER 7 PARAMETER ESTIMATION, DIAGNOSTIC
CHECKING, AND MODEL SELECTION 136 7.1 THE METHOD OF MOMENTS 136 7.2
MAXIMUM LIKELIHOOD METHOD 138 7.2.1 CONDITIONAL MAXIMUM LIKELIHOOD
ESTIMATION 138 7.2.2 UNCONDITIONAL MAXIMUM LIKELIHOOD ESTIMATION AND
BACKCASTING METHOD 140 7.2.3 EXACT LIKELIHOOD FUNCTIONS 143 7.3
NONLINEAR ESTIMATION 145 7.4 ORDINARY LEAST SQUARES (OLS) ESTIMATION IN
TIME SERIES ANALYSIS 151 7.5 DIAGNOSTIC CHECKING 152 7.6 EMPIRICAL
EXAMPLES FOR SERIES W1-W7 153 7.7 MODEL SELECTION CRITERIA 156 EXERCISES
158 CHAPTER 8 SEASONAL TIME SERIES MODELS 8.1 GENERAL CONCEPTS 8.2
TRADITIONAL METHODS 8.2.1 REGRESSION METHOD 8.2.2 MOVING AVERAGE METHOD
8.3 SEASONAL ARIMA MODELS 8.4 EMPIRICAL EXAMPLES EXERCISES 160 160 162
162 163 164 170 182 CHAPTER 9 TESTING FOR A UNIT ROOT 186 9.1
INTRODUCTION 186 9.2 SOME USEFUL LIMITING DISTRIBUTIONS 186 9.3 TESTING
FOR A UNIT ROOT IN THE AR( 1) MODEL 189 9.3.1 TESTING THE AR(1) MODEL
WITHOUT A CONSTANT TERM 189 9.3.2 TESTING THE AR( 1) MODEL WITH A
CONSTANT TERM 192 9.3.3 TESTING THE AR( 1) MODEL WITH A LINEAR TIME
TREND 195 9.4 TESTING FOR A UNIT ROOT IN A MORE GENERAL MODEL 196 XII
CONTENTS 9.5 TESTING FOR A UNIT ROOT IN SEASONAL TIME SERIES MODELS 206
9.5.1 TESTING THE SIMPLE ZERO MEAN SEASONAL MODEL 207 9.5.2 TESTING THE
GENERAL MULTIPLICATIVE ZERO MEAN SEASONAL MODEL 207 EXERCISES 211
CHAPTER 10 INTERVENTION ANALYSIS AND OUTLIER DETECTION 212 10.1
INTERVENTION MODELS 212 10.2 EXAMPLES OF INTERVENTION ANALYSIS 215 10.3
TIME SERIES OUTLIERS 223 10.3.1 ADDITIVE AND INNOVATIONAL OUTLIERS 223
10.3.2 ESTIMATION OF THE OUTLIER EFFECT WHEN THE TIMING OF THE OUTLIER
IS KNOWN 224 10.3.3 DETECTION OF OUTLIERS USING AN ITERATIVE PROCEDURE
226 10.4 EXAMPLES OF OUTLIER ANALYSIS 228 10.5 MODEL IDENTIFICATION IN
THE PRESENCE OF OUTLIERS 230 EXERCISES 235 CHAPTER 11 FOURIER ANALYSIS
237 11.1 GENERAL CONCEPTS 237 11.2 ORTHOGONAL FUNCTIONS 238 11.3 FOURIER
REPRESENTATION OF FINITE SEQUENCES 241 11.4 FOURIER REPRESENTATION OF
PERIODIC SEQUENCES 242 11.5 FOURIER REPRESENTATION OF NONPERIODIC
SEQUENCES: THE DISCRETE-TIME FOURIER TRANSFORM 247 11.6 FOURIER
REPRESENTATION OF CONTINUOUS-TIME FUNCTIONS 254 11.6.1 FOURIER
REPRESENTATION OF PERIODIC FUNCTIONS 254 11.6.2 FOURIER REPRESENTATION
OF NONPERIODIC FUNCTIONS: THE CONTINUOUS-TIME FOURIER TRANSFORM 256 11.7
THE FAST FOURIER TRANSFORM 258 EXERCISES 261 CHAPTER 12 SPECTRAL THEORY
OF STATIONARY PROCESSES 264 12.1 THE SPECTRUM 264 12.1.1 THE SPECTRUM
AND ITS PROPERTIES 264 12.1.2 THE SPECTRAL REPRESENTATION OF
AUTOCOVARIANCE FUNCTIONS: THE SPECTRAL DISTRIBUTION FUNCTION 267 12.1.3
WOLD S DECOMPOSITION OF A STATIONARY PROCESS 271 12.1.4 THE SPECTRAL
REPRESENTATION OF STATIONARY PROCESSES 272 CONTENTS XIII 12.2 THE
SPECTRUM OF SOME COMMON PROCESSES 274 12.2.1 THE SPECTRUM AND THE
AUTOCOVARIANCE GENERATING FUNCTION 274 12.2.2 THE SPECTRUM OF ARMA
MODELS 274 12.2.3 THE SPECTRUM OF THE SUM OF TWO INDEPENDENT PROCESSES
278 12.2.4 THE SPECTRUM OF SEASONAL MODELS 279 12.3 THE SPECTRUM OF
LINEAR FILTERS 281 | 12.3.1 THE FILTER FUNCTION 281 1 12.3.2 EFFECT OF
MOVING AVERAGE 283 12.3.3 EFFECT OF DIFFERENCING 284 12.4 ALIASING 285
EXERCISES 286 CHAPTER 13 ESTIMATION OF THE SPECTRUM 289 13.1 PERIODOGRAM
ANALYSIS 289 13.1.1 THE PERIODOGRAM 289 13.1.2 SAMPLING PROPERTIES OF
THE PERIODOGRAM 290 13.1.3 TESTS FOR HIDDEN PERIODIC COMPONENTS 292 13.2
THE SAMPLE SPECTRUM 298 13.3 THE SMOOTHED SPECTRUM 301 13.3.1 SMOOTHING
IN THE FREQUENCY DOMAIN: THE SPECTRAL WINDOW 301 13.3.2 SMOOTHING IN THE
TIME DOMAIN: THE LAG WINDOW 304 13.3.3 SOME COMMONLY USED WINDOWS 306
13.3.4 APPROXIMATE CONFIDENCE INTERVALS FOR SPECTRAL ORDINATES 313 13.4
ARMA SPECTRAL ESTIMATION 318 EXERCISES 321 CHAPTER 14 TRANSFER FUNCTION
MODELS 322 14.1 SINGLE-INPUT TRANSFER FUNCTION MODELS 322 14.1.1 GENERAL
CONCEPTS 322 14.1.2 SOME TYPICAL IMPULSE RESPONSE FUNCTIONS 324 14.2 THE
CROSS-CORRELATION FUNCTION AND TRANSFER FUNCTION MODELS 325 14.2.1 THE
CROSS-CORRELATION FUNCTION (CCF) 325 14.2.2 THE RELATIONSHIP BETWEEN THE
CROSS-CORRELATION FUNCTION AND THE TRANSFER FUNCTION 328 14.3
CONSTRUCTION OF TRANSFER FUNCTION MODELS 329 14.3.1 SAMPLE
CROSS-CORRELATION FUNCTION 329 14.3.2 IDENTIFICATION OF TRANSFER
FUNCTION MODELS 331 14.3.3 ESTIMATION OF TRANSFER FUNCTION MODELS 332
XIV CONTENTS 14.3.4 DIAGNOSTIC CHECKING OF TRANSFER FUNCTION MODELS 334
14.3.5 AN EMPIRICAL EXAMPLE 335 14.4 FORECASTING USING TRANSFER FUNCTION
MODELS 341 14.4.1 MINIMUM MEAN SQUARE ERROR FORECASTS FOR STATIONARY
INPUT AND OUTPUT SCRIES 342 14.4.2 MINIMUM MEAN SQUARE ERROR FORECASTS
FOR NONSTATIONARY INPUT AND OUTPUT SERIES 343 14.4.3 AN EXAMPLE 346 14.5
BIVARIATE FREQUENCY-DOMAIN ANALYSIS 349 14.5.1 CROSS-COVARIANCE
GENERATING FUNCTIONS AND THE CROSS-SPECTRUM 349 14.5.2 INTERPRETATION OF
THE CROSS-SPECTRAL FUNCTIONS 351 14.5.3 EXAMPLES 355 14.5.4 ESTIMATION
OF THE CROSS-SPECTRUM 357 14.6 THE CROSS-SPECTRUM AND TRANSFER FUNCTION
MODELS 359 14.6.1 CONSTRUCTION OF TRANSFER FUNCTION MODELS THROUGH
CROSS-SPECTRUM ANALYSIS 359 14.6.2 CROSS-SPECTRAL FUNCTIONS OF TRANSFER
FUNCTION MODELS 360 14.7 MULTIPLE-INPUT TRANSFER FUNCTION MODELS 361
EXERCISES 363 CHAPTER 15 TIME SERIES REGRESSION AND GARCH MODELS 366
15.1 REGRESSION WITH AUTOCORRELATED ERRORS 366 15.2 ARCH AND GARCH
MODELS 368 15.3 ESTIMATION OF GARCH MODELS 373 15.3.1 MAXIMUM LIKELIHOOD
ESTIMATION 373 15.3.2 ITERATIVE ESTIMATION 374 15.4 COMPUTATION OF
FORECAST ERROR VARIANCE 375 15.5 ILLUSTRATIVE EXAMPLES 376 EXERCISES 380
CHAPTER 16 VECTOR TIME SERIES MODELS 382 16.1 COVARIANCE AND CORRELATION
MATRIX FUNCTIONS 382 16.2 MOVING AVERAGE AND AUTOREGRESSIVE
REPRESENTATIONS OF VECTOR PROCESSES 384 16.3 THE VECTOR AUTOREGRESSIVE
MOVING AVERAGE PROCESS 386 16.3.1 COVARIANCE MATRIX FUNCTION FOR THE
VECTOR AR(1) MODEL 391 16.3.2 VECTOR AR(P) MODELS 394 16.3.3 VECTOR
MA(1) MODELS 396 16.3.4 VECTOR MA(Q) MODELS 397 16.3.5 VECTOR ARMA( 1,
1) MODELS 398 CONTENTS XV 16.4 NONSTATIONARY VECTOR AULOREGRESSIVE
MOVING AVERAGE MODELS 400 16.5 IDENTIFICATION OF VECTOR TIME SERIES
MODELS 401 16.5.1 SAMPLE CORRELATION MATRIX FUNCTION 401 [ 16.5.2
PARTIAL AUTOREGRESSION MATRICES 402 | 16.5.3 PARTIAL LAG CORRELATION
MATRIX FUNCTION 408 J 16.6 MODEL FITTING AND FORECASTING 414 I 16.7 AN
EMPIRICAL EXAMPLE 416 [ 16.7.1 MODEL IDENTIFICATION 417 16.7.2 PARAMETER
ESTIMATION 417 16.7.3 DIAGNOSTIC CHECKING 420 16.7.4 FORECASTING 420
16.7.5 FURTHER REMARKS 421 16.8 SPECTRAL PROPERTIES OF VECTOR PROCESSES
421 SUPPLEMENT 16.A MULTIVARIATE LINEAR REGRESSION MODELS 423 EXERCISES
426 CHAPTER 17 MORE ON VECTOR TIME SERIES 428 17.1 UNIT ROOTS AND
COINTEGRATION IN VECTOR PROCESSES 428 17.1.1 REPRESENTATIONS OF
NONSTATIONARY COINTEGRATED PROCESSES 430 17.1.2 DECOMPOSITION OF Z, 434
17.1.3 TESTING AND ESTIMATING COINTEGRATION 435 17.2 PARTIAL PROCESS AND
PARTIAL PROCESS CORRELATION MATRICES 442 17.2.1 COVARIANCE MATRIX
GENERATING FUNCTION 442 17.2.2 PARTIAL COVARIANCE MATRIX GENERATING
FUNCTION 443 17.2.3 PARTIAL PROCESS SAMPLE CORRELATION MATRIX FUNCTIONS
447 17.2.4 AN EMPIRICAL EXAMPLE: THE U.S. HOG DATA 448 17.3 EQUIVALENT
REPRESENTATIONS OF A VECTOR ARMA MODEL 451 17.3.1 FINITE-ORDER
REPRESENTATIONS OF A VECTOR TIME SERIES PROCESS 453 17.3.2 SOME
IMPLICATIONS 457 EXERCISES 460 CHAPTER 18 STATE SPACE MODELS AND THE
KALMAN FILTER 463 18.1 STATE SPACE REPRESENTATION 463 18.2 THE
RELATIONSHIP BETWEEN STATE SPACE AND ARMA MODELS 464 18.3 STATE SPACE
MODEL FITTING AND CANONICAL CORRELATION ANALYSIS 470 18.4 EMPIRICAL
EXAMPLES 474 18.5 THE KALMAN FILTER AND ITS APPLICATIONS 478 SUPPLEMENT
18.A CANONICAL CORRELATIONS 483 EXERCISES 487 XVI CONTENTS CHAPTER 19
LONG MEMORY AND NONLINEAR PROCESSES 489 19.1 LONG MEMORY PROCESSES AND
FRACTIONAL DIFFERENCING 489 19.1.1 FRACTIONALLY INTEGRATED ARMA MODELS
AND THEIR ACF 489 19.1.2 PRACTICAL IMPLICATIONS OF THE ARFIMA PROCESSES
492 19.1.3 ESTIMATION OF THE FRACTIONAL DIFFERENCE 492 19.2 NONLINEAR
PROCESSES 494 19.2.1 CUMULANTS, POLYSPECTRUM, AND TESTS FOR LINEARITY
AND NORMALITY 495 19.2.2 SOME NONLINEAR TIME SERIES MODELS 498 19.3
THRESHOLD AUTOREGRESSIVE MODELS 499 19.3.1 TESTS FOR TAR MODELS 500
19.3.2 MODELING TAR MODELS 502 EXERCISES 506 CHAPTER 20 AGGREGATION AND
SYSTEMATIC SAMPLING IN TIME SERIES 507 20.1 TEMPORAL AGGREGATION OF THE
ARIMA PROCESS 507 20.1.1 THE RELATIONSHIP OF AUTOCOVARIANCES BETWEEN THE
NONAGGREGATE AND AGGREGATE SERIES 508 20.1.2 TEMPORAL AGGREGATION OF THE
IMA(J, Q) PROCESS 511 20.1.3 TEMPORAL AGGREGATION OF THE AR(P) PROCESS
512 20.1.4 TEMPORAL AGGREGATION OF THE ARIMA(P, D, Q) PROCESS 513 20.1.5
THE LIMITING BEHAVIOR OF TIME SERIES AGGREGATES 515 20.2 THE EFFECTS OF
AGGREGATION ON FORECASTING AND PARAMETER ESTIMATION 520 20.2.1 HILBERT
SPACE 520 20.2.2 THE APPLICATION OF HILBERT SPACE IN FORECASTING 521
20.2.3 THE EFFECT OF TEMPORAL AGGREGATION ON FORECASTING 522 20.2.4
INFORMATION LOSS DUE TO AGGREGATION IN PARAMETER ESTIMATION 524 20.3
SYSTEMATIC SAMPLING OF THE ARIMA PROCESS 526 20.4 THE EFFECTS OF
SYSTEMATIC SAMPLING AND TEMPORAL AGGREGATION ON CAUSALITY 528 20.4.1
DECOMPOSITION OF LINEAR RELATIONSHIP BETWEEN TWO TIME SERIES 528 20.4.2
AN ILLUSTRATIVE UNDERLYING MODEL 531 20.4.3 THE EFFECTS OF SYSTEMATIC
SAMPLING AND TEMPORAL AGGREGATION ON CAUSALITY 532 20.5 THE EFFECTS OF
AGGREGATION ON TESTING FOR LINEARITY AND NORMALITY 534 20.5.1 TESTING
FOR LINEARITY AND NORMALITY 534 20.5.2 THE EFFECTS OF TEMPORAL
AGGREGATION ON TESTING FOR LINEARITY AND NORMALITY 537 CONTENTS XVII
20.6 THE EFFECTS OF AGGREGATION ON TESTING FOR A UNIT ROOT 540 20.6.1
THE MODEL OF AGGREGATE SCRIES 541 20.6.2 THE EFFECTS OF AGGREGATION ON
THE DISTRIBUTION OF THE TEST STATISTICS 542 20.6.3 THE EFFECTS OF
AGGREGATION ON THE SIGNIFICANCE LEVEL AND THE POWER OF THE TEST 543
20.6.4 EXAMPLES 545 20.6.5 GENERAL CASES AND CONCLUDING REMARKS 547 20.7
FURTHER COMMENTS 549 EXERCISES 550 REFERENCES 553 APPENDIX 565 TIME
SERIES DATA USED FOR ILLUSTRATIONS 565 STATISTICAL TABLES 565 AUTHOR
INDEX 601 SUBJECT INDEX 605
|
| any_adam_object | 1 |
| author | Wei, William W. S. 1940- |
| author_GND | (DE-588)17014495X |
| author_facet | Wei, William W. S. 1940- |
| author_role | aut |
| author_sort | Wei, William W. S. 1940- |
| author_variant | w w s w wws wwsw |
| building | Verbundindex |
| bvnumber | BV020040557 |
| callnumber-first | Q - Science |
| callnumber-label | QA 280 |
| callnumber-raw | QA 280 |
| callnumber-search | QA 280 |
| callnumber-sort | QA 3280 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | QH 237 SK 845 |
| classification_tum | MAT 634f |
| ctrlnum | (OCoLC)56559354 (DE-599)BVBBV020040557 |
| dewey-full | 519.5/5 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/5 |
| dewey-search | 519.5/5 |
| dewey-sort | 3519.5 15 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik Wirtschaftswissenschaften |
| edition | 2. ed. |
| format | Book |
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| id | DE-604.BV020040557 |
| illustrated | Illustrated |
| indexdate | 2024-07-09T20:11:29Z |
| institution | BVB |
| isbn | 0321322169 |
| language | English |
| lccn | 2004058701 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-013361647 |
| oclc_num | 56559354 |
| open_access_boolean | |
| owner | DE-945 DE-634 DE-91G DE-BY-TUM DE-M347 DE-521 DE-29T DE-473 DE-BY-UBG DE-92 |
| owner_facet | DE-945 DE-634 DE-91G DE-BY-TUM DE-M347 DE-521 DE-29T DE-473 DE-BY-UBG DE-92 |
| physical | XXII, 614 S. graph. Darst. |
| publishDate | 2006 |
| publishDateSearch | 2006 |
| publishDateSort | 2006 |
| publisher | Pearson Addison Wesley |
| record_format | marc |
| spelling | Wei, William W. S. 1940- Verfasser (DE-588)17014495X aut Time series analysis univariate and multivariate methods William W. S. Wei 2. ed. Boston [u.a.] Pearson Addison Wesley 2006 XXII, 614 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Análise de séries temporais larpcal Série chronologique aTime-series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s DE-604 HEBIS Datenaustausch Darmstadt application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013361647&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Wei, William W. S. 1940- Time series analysis univariate and multivariate methods Análise de séries temporais larpcal Série chronologique aTime-series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4067486-1 |
| title | Time series analysis univariate and multivariate methods |
| title_auth | Time series analysis univariate and multivariate methods |
| title_exact_search | Time series analysis univariate and multivariate methods |
| title_full | Time series analysis univariate and multivariate methods William W. S. Wei |
| title_fullStr | Time series analysis univariate and multivariate methods William W. S. Wei |
| title_full_unstemmed | Time series analysis univariate and multivariate methods William W. S. Wei |
| title_short | Time series analysis |
| title_sort | time series analysis univariate and multivariate methods |
| title_sub | univariate and multivariate methods |
| topic | Análise de séries temporais larpcal Série chronologique aTime-series analysis Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Análise de séries temporais Série chronologique aTime-series analysis Zeitreihenanalyse |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=013361647&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT weiwilliamws timeseriesanalysisunivariateandmultivariatemethods |