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Time Series Modelling in Earth Sciences de B.K. Sahu

Time Series Modelling in Earth Sciences

Autor B.K. Sahu
en Limba Engleză Hardback – 2003
Including the latest theories and applications of time series modelling, this book is intended for students, faculties and professionals with a background in multivariate statistics.
Highlighting linear methods to yield ARIMA, SARIMA models and their multivariate (vector) extensions, the text also draws attention to non-linear methods, as well as state-space, dynamic linear, wavelet, volatility and long memory models. Also included are several solved case studies and exercises from the fields of mining, ore genesis, earthquakes, and climatology.
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Specificații

ISBN-13: 9789058092670
ISBN-10: 9058092674
Pagini: 294
Dimensiuni: 156 x 234 x 18 mm
Greutate: 0.68 kg
Ediția:1
Editura: CRC Press
Colecția CRC Press

Notă biografică

B.K. Sahu Department of Earth Sciences, IIT Bombay, Mumbai, India

Cuprins

1. Introduction 1.1 Dynamic Earth 1.2 Time/Spatial Series 1.3 Time Domain Analysis 1.4 Frequency Domain Analysis 1.5 Spectral Analysis 1.6 Simple Time Series Models 1.7 Model Validation and Inferences 1.8 State-Space Model and Kalman Filtering 1.9 Procedures for Time Series Data Analysis 1.10 Examples 2. Stationary Univariate Time Series Models 2.1 Introduction 2.2 Stationary Processes 2.3 Identification 2.4 Forecasting 2.5 Purely Seasonal ARMA Models ARMA(P, Q)s 2.6 Model Genesis and Realization 2.7 Examples 3. Non-Stationary Univariate Time Series Models 3.1 Introduction 3.2 Transformations for Stationarity 3.3 Unit Root Problems 3.4 Forecasting ARIMA Models 3.5 SARIMA Models 4. Vector and Multidimensional Time Series Analysis 4.1 Introduction 4.2 Multivariate ARMA Processes 4.3 Transfer Function Models 4.4 Forecasting Techniques 4.5 Some Case Studies from Economic Geology 5. Advanced Time Series Models 5.1 Introduction 5.2 State-Space Models 5.3 Dynamic Linear Modelling (DLM) with Switching 5.4 Non-Linear Models

Descriere

Exploring the most up-to-date theories and applications of time series modelling, this text includes reference to both linear and non-linear methods, and constitutes an invaluable tool for those involved in the field of multivariate statistics.