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| 900 |
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| 900 |
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| 900 |
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| 900 |
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| 900 |
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| 900 |
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| 900 |
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| 900 |
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| 950 |
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| 950 |
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|
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| 950 |
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|
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| 950 |
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|
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| 950 |
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| 950 |
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| 950 |
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|
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| contents |
It is divided into four substantive parts, as follows: Part 1, Stochastic Models and their Forecasting, is an introduction to linear stationary models with finite numbers of parameters, in particular ARMA (mixed autoregressive-moving average) processes, their probabilistic and forecasting properties. It is a feature of the book that time-domain considerations prevail, against frequency-domain arguments. Part 2, Stochastic Model Building, covers the basic statistical approach. Here, non-stationary series are considered, but such that suitable differencing reduces them to stationarity: these are the ARIMA (I for integrated) models. Empirical analysis is done by intensively using a given data set (time series), in cycles of identification, estimation and checking. The final chapter in this part applies these elements to seasonal models. Part 3 extends the approach to Transfer Function Models; their probabilistic structure is described, previous to empirical fitting. An interesting new chapter in this part is devoted to intervention analysis and outlier detection. Part 4 is on Design of Discrete Control Schemes. The final portion of the book collects tables, charts, data sets, exercises and problems, references and a detailed index.\par In relation to the original edition, there are modifications in the chapter on estimation, and additions such as canonical correlation analysis, use of model selection criteria, testing for unit roots, nonstationarity in ARIMA processes, state representation of ARMA models, score tests, structural components, and others.\par The first edition of this book appeared in 1970, see the review Zbl 0249.62009, at a time when comparatively few book on time series were available. It proposed a practical approach to the empirical analysis, that was adopted by many statisticians and workers in a wide diversity of fields. In the intervening 25 years, the field of statistical time series grew enormously: many books and monographs were published, new journals were created, and the number of specialists and users of the methods grew accordingly. It is easy to predict that this new edition will hold a key place in the time series area. The book has been considerably updated, retaining the initial level and approach.\par Gwilym M. Jenkins, co-author of the first edition, died several years ago, and the present edition, that incorporates G. C. Reinsel as co-author, is dedicated to his memory. |
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Literaturverz. S. 556 - 567 |
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Mathematisches Modell, Statistische Methodenlehre, Ökonometrie, Zeitreihenanalyse, Transfer (Lernen), Wahrscheinlichkeitstheorie, Entscheidungstheorie, Kontrolltheorie, Rückkopplung |
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Box, George E. P. 1919-2013 (DE-588)108415066 (DE-627)481945792 (DE-576)160392365 aut, Time series analysis forecasting and control George E. P. Box; Gwilym M. Jenkins; Gregory C. Reinsel, 3. ed., Englewood Cliffs, NJ [u.a.] Prentice-Hall 1994, XVI, 598 S. graph. Darst., Text txt rdacontent, ohne Hilfsmittel zu benutzen n rdamedia, Band nc rdacarrier, Literaturverz. S. 556 - 567, It is divided into four substantive parts, as follows: Part 1, Stochastic Models and their Forecasting, is an introduction to linear stationary models with finite numbers of parameters, in particular ARMA (mixed autoregressive-moving average) processes, their probabilistic and forecasting properties. It is a feature of the book that time-domain considerations prevail, against frequency-domain arguments. Part 2, Stochastic Model Building, covers the basic statistical approach. Here, non-stationary series are considered, but such that suitable differencing reduces them to stationarity: these are the ARIMA (I for integrated) models. Empirical analysis is done by intensively using a given data set (time series), in cycles of identification, estimation and checking. The final chapter in this part applies these elements to seasonal models. Part 3 extends the approach to Transfer Function Models; their probabilistic structure is described, previous to empirical fitting. An interesting new chapter in this part is devoted to intervention analysis and outlier detection. Part 4 is on Design of Discrete Control Schemes. The final portion of the book collects tables, charts, data sets, exercises and problems, references and a detailed index.\par In relation to the original edition, there are modifications in the chapter on estimation, and additions such as canonical correlation analysis, use of model selection criteria, testing for unit roots, nonstationarity in ARIMA processes, state representation of ARMA models, score tests, structural components, and others.\par The first edition of this book appeared in 1970, see the review Zbl 0249.62009, at a time when comparatively few book on time series were available. It proposed a practical approach to the empirical analysis, that was adopted by many statisticians and workers in a wide diversity of fields. In the intervening 25 years, the field of statistical time series grew enormously: many books and monographs were published, new journals were created, and the number of specialists and users of the methods grew accordingly. It is easy to predict that this new edition will hold a key place in the time series area. The book has been considerably updated, retaining the initial level and approach.\par Gwilym M. Jenkins, co-author of the first edition, died several years ago, and the present edition, that incorporates G. C. Reinsel as co-author, is dedicated to his memory., Mathematisches Modell, Statistische Methodenlehre, Ökonometrie, Zeitreihenanalyse, Transfer (Lernen), Wahrscheinlichkeitstheorie, a Time-series analysis, a Prediction theory, a Transfer functions, a Feedback control systems Mathematical models, s (DE-588)4067486-1 (DE-627)106108840 (DE-576)209169990 Zeitreihenanalyse gnd, DE-101, s (DE-588)4138606-1 (DE-627)104137150 (DE-576)209687460 Entscheidungstheorie gnd, s (DE-588)4032317-1 (DE-627)106262254 (DE-576)208996389 Kontrolltheorie gnd, s (DE-588)4050851-1 (DE-627)106179756 (DE-576)209090057 Rückkopplung gnd, s (DE-588)4114528-8 (DE-627)105821527 (DE-576)209485361 Mathematisches Modell gnd, (DE-627), Jenkins, Gwilym M. 1932-1982 (DE-588)131749374 (DE-627)513599991 (DE-576)298715023 aut, Reinsel, Gregory C. 1948-2004 (DE-588)113599382 (DE-627)538258187 (DE-576)289767962 aut, http://www.gbv.de/dms/hbz/toc/ht006250030.pdf V:DE-605 pdf/application 2008-11-15 Verlag Inhaltsverzeichnis Inhaltsverzeichnis, https://zbmath.org/?q=an:0858.62072 B:ZBM 2021-04-12 Verlag Zentralblatt MATH Inhaltstext, (DE-627)257179941, (DE-627)067801390, DE-14 epn:3282642598 2009-02-07T07:11:59Z, DE-14 epn:3282642601 2009-02-07T07:11:59Z, DE-105 epn:3282643047 2001-10-25T00:00:00Z, DE-Zi4 epn:328264311X e 2003-02-12T00:00:00Z |
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Box, George E. P., Jenkins, Gwilym M., Reinsel, Gregory C., Time series analysis: forecasting and control, It is divided into four substantive parts, as follows: Part 1, Stochastic Models and their Forecasting, is an introduction to linear stationary models with finite numbers of parameters, in particular ARMA (mixed autoregressive-moving average) processes, their probabilistic and forecasting properties. It is a feature of the book that time-domain considerations prevail, against frequency-domain arguments. Part 2, Stochastic Model Building, covers the basic statistical approach. Here, non-stationary series are considered, but such that suitable differencing reduces them to stationarity: these are the ARIMA (I for integrated) models. Empirical analysis is done by intensively using a given data set (time series), in cycles of identification, estimation and checking. The final chapter in this part applies these elements to seasonal models. Part 3 extends the approach to Transfer Function Models; their probabilistic structure is described, previous to empirical fitting. An interesting new chapter in this part is devoted to intervention analysis and outlier detection. Part 4 is on Design of Discrete Control Schemes. The final portion of the book collects tables, charts, data sets, exercises and problems, references and a detailed index.\par In relation to the original edition, there are modifications in the chapter on estimation, and additions such as canonical correlation analysis, use of model selection criteria, testing for unit roots, nonstationarity in ARIMA processes, state representation of ARMA models, score tests, structural components, and others.\par The first edition of this book appeared in 1970, see the review Zbl 0249.62009, at a time when comparatively few book on time series were available. It proposed a practical approach to the empirical analysis, that was adopted by many statisticians and workers in a wide diversity of fields. In the intervening 25 years, the field of statistical time series grew enormously: many books and monographs were published, new journals were created, and the number of specialists and users of the methods grew accordingly. It is easy to predict that this new edition will hold a key place in the time series area. The book has been considerably updated, retaining the initial level and approach.\par Gwilym M. Jenkins, co-author of the first edition, died several years ago, and the present edition, that incorporates G. C. Reinsel as co-author, is dedicated to his memory., Mathematisches Modell, Statistische Methodenlehre, Ökonometrie, Zeitreihenanalyse, Transfer (Lernen), Wahrscheinlichkeitstheorie, a, Time-series analysis, Prediction theory, Transfer functions, Feedback control systems, Mathematical models, Entscheidungstheorie, Kontrolltheorie, Rückkopplung |
| swb_id_str |
042733219 |
| title |
Time series analysis: forecasting and control |
| title_auth |
Time series analysis forecasting and control |
| title_full |
Time series analysis forecasting and control George E. P. Box; Gwilym M. Jenkins; Gregory C. Reinsel |
| title_fullStr |
Time series analysis forecasting and control George E. P. Box; Gwilym M. Jenkins; Gregory C. Reinsel |
| title_full_unstemmed |
Time series analysis forecasting and control George E. P. Box; Gwilym M. Jenkins; Gregory C. Reinsel |
| title_short |
Time series analysis |
| title_sort |
time series analysis forecasting and control |
| title_sub |
forecasting and control |
| title_unstemmed |
Time series analysis: forecasting and control |
| topic |
Mathematisches Modell, Statistische Methodenlehre, Ökonometrie, Zeitreihenanalyse, Transfer (Lernen), Wahrscheinlichkeitstheorie, a, Time-series analysis, Prediction theory, Transfer functions, Feedback control systems, Mathematical models, Entscheidungstheorie, Kontrolltheorie, Rückkopplung |
| topic_facet |
Mathematisches Modell, Statistische Methodenlehre, Ökonometrie, Zeitreihenanalyse, Transfer (Lernen), Wahrscheinlichkeitstheorie, Entscheidungstheorie, Kontrolltheorie, Rückkopplung, a, Time-series analysis, Prediction theory, Transfer functions, Feedback control systems, Mathematical models |
| udk_facet_de105 |
Mathematik |
| udk_raw_de105 |
DK 519.246 |
| url |
http://www.gbv.de/dms/hbz/toc/ht006250030.pdf, https://zbmath.org/?q=an:0858.62072 |
| work_keys_str_mv |
AT boxgeorgeep timeseriesanalysisforecastingandcontrol, AT jenkinsgwilymm timeseriesanalysisforecastingandcontrol, AT reinselgregoryc timeseriesanalysisforecastingandcontrol |
