An introduction to time series modeling
| Författare | |
|---|---|
| Förlag | Studentlitteratur AB |
| Genre | Matematik och statistik |
| Format | Häftad |
| Språk | Engelska |
| Antal sidor | 393 |
| Vikt | 550 gr |
| Utgiven | 2021-11-18 |
| SAB | Thie |
| ISBN | 9789144158945 |
Time series analysis concerns the mathematical modeling of time varying phenomena, e.g., ocean waves, water levels in lakes and rivers, demand for electrical power, radar signals, muscular reactions, ECG-signals, or option prices at the stock market. This book gives a comprehensive presentation of stochastic models and methods in time series analysis.
The book treats stochastic vectors and both univariate and multivariate stochastic processes, as well as how these can be used to identify suitable models for various forms of observations. Furthermore, different approaches such as least squares, the prediction error method, and maximum likelihood are treated in detail, together with results on the Cramér-Rao lower bound, dictating the theoretically possible estimation accuracy. Residual analysis and prediction of stochastic models are also treated, as well as how one may form time-varying models, including the recursive least squares and the Kalman filter. The book discusses how to implement the various methods using Matlab, and several Matlab functions and data sets are provided with the book.
The book is aimed at advanced undergraduate and junior graduate students in statistics, mathematics, or engineering. Helpful prerequisites include courses in multivariate analysis, linear systems, basic probability, and stochastic processes.
The book treats stochastic vectors and both univariate and multivariate stochastic processes, as well as how these can be used to identify suitable models for various forms of observations. Furthermore, different approaches such as least squares, the prediction error method, and maximum likelihood are treated in detail, together with results on the Cramér-Rao lower bound, dictating the theoretically possible estimation accuracy. Residual analysis and prediction of stochastic models are also treated, as well as how one may form time-varying models, including the recursive least squares and the Kalman filter. The book discusses how to implement the various methods using Matlab, and several Matlab functions and data sets are provided with the book.
The book is aimed at advanced undergraduate and junior graduate students in statistics, mathematics, or engineering. Helpful prerequisites include courses in multivariate analysis, linear systems, basic probability, and stochastic processes.
