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State Space Modeling of Time Series, Masanao Aoki


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Автор: Masanao Aoki
Название:  State Space Modeling of Time Series
ISBN: 9783540528708
Издательство: Springer
Классификация:


ISBN-10: 3540528709
Обложка/Формат: Paperback
Страницы: 323
Вес: 0.56 кг.
Дата издания: 01.08.1990
Серия: Universitext
Язык: English
Размер: 244 x 170 x 19
Основная тема: Economics
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: In this book, the author adopts a state space approach to time series modeling to provide a new, computer-oriented method for building models for vector-valued time series.

Time Series Analysis by State Space Methods

Автор: Durbin, James; Koopman, Siem Jan
Название: Time Series Analysis by State Space Methods
ISBN: 019964117X ISBN-13(EAN): 9780199641178
Издательство: Oxford Academ
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Цена: 121440.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This new edition updates Durbin & Koopman`s important text on the state space approach to time series analysis providing a more comprehensive treatment, including the filtering of nonlinear and non-Gaussian series. The book provides an excellent source for the development of practical courses on time series analysis.

Time Series Analysis

Автор: Hamilton, James
Название: Time Series Analysis
ISBN: 0691042896 ISBN-13(EAN): 9780691042893
Издательство: Wiley
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Цена: 73920.00 T
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Описание: A graduate-level text which describes the recent dramatic changes that have taken place in the way that researchers analyze economic and financial time series. It explores such important innovations as vector regression, nonlinear time series models and the generalized methods of moments.

Time Series Modeling for Analysis and Control

Автор: Kohei Ohtsu; Hui Peng; Genshiro Kitagawa
Название: Time Series Modeling for Analysis and Control
ISBN: 4431553029 ISBN-13(EAN): 9784431553021
Издательство: Springer
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Цена: 46570.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Although ships` autopilot systems are considered through the entire book, the methods set forth in this book can be applied to many other complicated, large, or noisy feedback control systems for which it is difficult to derive a model of the entire system based on theory in that subject area.

Multivariate Time Series With Linear State Space Structure

Автор: V?ctor G?mez
Название: Multivariate Time Series With Linear State Space Structure
ISBN: 331928598X ISBN-13(EAN): 9783319285986
Издательство: Springer
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Цена: 88500.00 T
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Описание: This book presents a comprehensive study of multivariate time serieswith linear state space structure. The strength of the book also lies in the numerous algorithms includedfor state space models that take advantage of the recursive nature of themodels.

An Introduction to State Space Time Series Analysis

Автор: Commandeur, Jacques J.F.; Koopman, Siem Jan
Название: An Introduction to State Space Time Series Analysis
ISBN: 0199228876 ISBN-13(EAN): 9780199228874
Издательство: Oxford Academ
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Цена: 51210.00 T
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Описание: This text provides an introduction to time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. This is the first in a series of books designed to provide practitioners, researchers, and students with practical introductions to various topics in econometrics.

State-Space Methods for Time Series Analysis

Автор: Casals Jose Manuel Carro
Название: State-Space Methods for Time Series Analysis
ISBN: 148221959X ISBN-13(EAN): 9781482219593
Издательство: Taylor&Francis
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Цена: 102080.00 T
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Описание:

The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values.

Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form.

After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables.

Web Resource
The authors' E4 MATLAB(R) toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.


Nonlinear Modeling of Solar Radiation and Wind Speed Time Series

Автор: Fortuna
Название: Nonlinear Modeling of Solar Radiation and Wind Speed Time Series
ISBN: 3319387634 ISBN-13(EAN): 9783319387635
Издательство: Springer
Цена: 46570.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This brief is a clear, concise description of the main techniques of time series analysis —stationary, autocorrelation, mutual information, fractal and multifractal analysis, chaos analysis, etc.— as they are applied to the influence of wind speed and solar radiation on the production of electrical energy from these renewable sources. The problem of implementing prediction models is addressed by using the embedding-phase-space approach: a powerful technique for the modeling of complex systems. Readers are also guided in applying the main machine learning techniques for classification of the patterns hidden in their time series and so will be able to perform statistical analyses that are not possible by using conventional techniques.The conceptual exposition avoids unnecessary mathematical details and focuses on concrete examples in order to ensure a better understanding of the proposed techniques.Results are well-illustrated by figures and tables.

Applications of Computer Aided Time Series Modeling

Автор: Masanao Aoki; Arthur M. Havenner
Название: Applications of Computer Aided Time Series Modeling
ISBN: 0387947515 ISBN-13(EAN): 9780387947518
Издательство: Springer
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Цена: 111790.00 T
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Описание: To single out one of the several important insights in modeling that he shares with the reader, he discusses in Section 2ii the effects of sampling er- rors and model misspecification on successful modeling efforts.

Nonlinear Econometric Modeling in Time Series Analysis

Автор: Barnett William a., Hendry David F., Hylleberg Svend
Название: Nonlinear Econometric Modeling in Time Series Analysis
ISBN: 0521594243 ISBN-13(EAN): 9780521594240
Издательство: Cambridge Academ
Цена: 115110.00 T
Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book presents some of the more recent developments in nonlinear time series, including Bayesian analysis and cointegration tests.


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