State Space Modeling of Time Series:
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Berlin, Heidelberg
Springer Berlin Heidelberg
1987
|
| Schriftenreihe: | Universitext
|
| Schlagwörter: | |
| Links: | https://doi.org/10.1007/978-3-642-96985-0 |
| Beschreibung: | model's predictive capability? These are some of the questions that need to be answered in proposing any time series model construction method. This book addresses these questions in Part II. Briefly, the covariance matrices between past data and future realizations of time series are used to build a matrix called the Hankel matrix. Information needed for constructing models is extracted from the Hankel matrix. For example, its numerically determined rank will be the dimension of the state model. Thus the model dimension is determined by the data, after balancing several sources of error for such model construction. The covariance matrix of the model forecasting error vector is determined by solving a certain matrix Riccati equation. This matrix is also the covariance matrix of the innovation process which drives the model in generating model forecasts. In these model construction steps, a particular model representation, here referred to as balanced, is used extensively. This mode of model representation facilitates error analysis, such as assessing the error of using a lower dimensional model than that indicated by the rank of the Hankel matrix. The well-known Akaike's canonical correlation method for model construction is similar to the one used in this book. There are some important differences, however. Akaike uses the normalized Hankel matrix to extract canonical vectors, while the method used in this book does not normalize the Hankel matrix |
| Umfang: | 1 Online-Ressource (XI, 315 p) |
| ISBN: | 9783642969850 9783540172574 |
| ISSN: | 0172-5939 |
| DOI: | 10.1007/978-3-642-96985-0 |
Internformat
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| 500 | |a model's predictive capability? These are some of the questions that need to be answered in proposing any time series model construction method. This book addresses these questions in Part II. Briefly, the covariance matrices between past data and future realizations of time series are used to build a matrix called the Hankel matrix. Information needed for constructing models is extracted from the Hankel matrix. For example, its numerically determined rank will be the dimension of the state model. Thus the model dimension is determined by the data, after balancing several sources of error for such model construction. The covariance matrix of the model forecasting error vector is determined by solving a certain matrix Riccati equation. This matrix is also the covariance matrix of the innovation process which drives the model in generating model forecasts. In these model construction steps, a particular model representation, here referred to as balanced, is used extensively. This mode of model representation facilitates error analysis, such as assessing the error of using a lower dimensional model than that indicated by the rank of the Hankel matrix. The well-known Akaike's canonical correlation method for model construction is similar to the one used in this book. There are some important differences, however. Akaike uses the normalized Hankel matrix to extract canonical vectors, while the method used in this book does not normalize the Hankel matrix | ||
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Datensatz im Suchindex
| DE-BY-TUM_katkey | 2070159 |
|---|---|
| _version_ | 1821931356212428800 |
| any_adam_object | |
| author | Aoki, Masanao 1931-2018 |
| author_GND | (DE-588)170079929 |
| author_facet | Aoki, Masanao 1931-2018 |
| author_role | aut |
| author_sort | Aoki, Masanao 1931-2018 |
| author_variant | m a ma |
| building | Verbundindex |
| bvnumber | BV042423150 |
| classification_tum | MAT 000 |
| collection | ZDB-2-SMA ZDB-2-BAE |
| ctrlnum | (OCoLC)864064948 (DE-599)BVBBV042423150 |
| dewey-full | 330.1 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 330 - Economics |
| dewey-raw | 330.1 |
| dewey-search | 330.1 |
| dewey-sort | 3330.1 |
| dewey-tens | 330 - Economics |
| discipline | Mathematik Wirtschaftswissenschaften |
| doi_str_mv | 10.1007/978-3-642-96985-0 |
| format | Electronic eBook |
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| id | DE-604.BV042423150 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T17:10:48Z |
| institution | BVB |
| isbn | 9783642969850 9783540172574 |
| issn | 0172-5939 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027858567 |
| oclc_num | 864064948 |
| open_access_boolean | |
| owner | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
| owner_facet | DE-384 DE-703 DE-91 DE-BY-TUM DE-634 |
| physical | 1 Online-Ressource (XI, 315 p) |
| psigel | ZDB-2-SMA ZDB-2-BAE ZDB-2-SMA_Archive |
| publishDate | 1987 |
| publishDateSearch | 1987 |
| publishDateSort | 1987 |
| publisher | Springer Berlin Heidelberg |
| record_format | marc |
| series2 | Universitext |
| spellingShingle | Aoki, Masanao 1931-2018 State Space Modeling of Time Series Economics Economics/Management Science Economic Theory Management Wirtschaft Zeitreihenanalyse (DE-588)4067486-1 gnd Modell (DE-588)4039798-1 gnd Zustandsraum (DE-588)4132647-7 gnd Zeitreihe (DE-588)4127298-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4067486-1 (DE-588)4039798-1 (DE-588)4132647-7 (DE-588)4127298-5 (DE-588)4123037-1 |
| title | State Space Modeling of Time Series |
| title_auth | State Space Modeling of Time Series |
| title_exact_search | State Space Modeling of Time Series |
| title_full | State Space Modeling of Time Series by Masanao Aoki |
| title_fullStr | State Space Modeling of Time Series by Masanao Aoki |
| title_full_unstemmed | State Space Modeling of Time Series by Masanao Aoki |
| title_short | State Space Modeling of Time Series |
| title_sort | state space modeling of time series |
| topic | Economics Economics/Management Science Economic Theory Management Wirtschaft Zeitreihenanalyse (DE-588)4067486-1 gnd Modell (DE-588)4039798-1 gnd Zustandsraum (DE-588)4132647-7 gnd Zeitreihe (DE-588)4127298-5 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Economics Economics/Management Science Economic Theory Management Wirtschaft Zeitreihenanalyse Modell Zustandsraum Zeitreihe Datenanalyse |
| url | https://doi.org/10.1007/978-3-642-96985-0 |
| work_keys_str_mv | AT aokimasanao statespacemodelingoftimeseries |