294 Pages
by
CRC Press
294 Pages
by
CRC Press
Also available as eBook on:
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.
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
Biography
B.K. Sahu Department of Earth Sciences, IIT Bombay, Mumbai, India
