| Review: |
This textbook deals with time series modelling for interpreting spatial and spatio-temporal data that are essentially dependent on previous values (samples). It explains how these time series models can be seasonal or nonseasonal (stationary or nonstationary), modelled as ARIMA or SARIMA, respectively. Nonlinear time series models, such as sudden bursts, threshold/exponential autoregressive, state–space models are used to solve more complex and realistic problems in earth sciences. Real geological case studies in economic geography, such as time series analysis of borehole assays and metal deposits in mines are included. Finally, techniques are described for more advanced and powerful advanced modelling for data forecasting, analysis and control. These have as yet not been applied to geological data analysis, but have potential for future use. |