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Time Series Modelling of Water Resources and Environmental Systems

Out of Print

Series: Developments in Water Science Volume: 45

By: KW Hipel and AI McLeod

Elsevier

Hardback | Dec 1994 | #84659 | ISBN: 0444892702
Out of Print Details

About this book

This is a comprehensive presentation of the theory and practice of time series modelling of environmental systems. A variety of time series models are explained and illustrated, including ARMA (autoregressive-moving average), nonstationary, long memory, three families of seasonal, multiple input-single output, intervention and multivariate ARMA models. Other topics in environmetrics covered in this book include time series analysis in decision making, estimating missing observations, simulation, the Hurst phenomenon, forecasting experiments and causality. Professionals working in fields overlapping with environmetrics - such as water resources engineers, environmental scientists, hydrologists, geophysicists, geographers, earth scientists and planners - will find this book a valuable resource. Equally outwith environmetrics, systems scientists, economists, mechanical engineers, chemical engineers, and management scientists will find the time series methods presented in this book useful.


Contents

Part 1 Scope and background material: 1. Environmetrics, Science and Decision Making. 2. Basic Statistical Concepts. Part 2 Linear nonseasonal models: 3. Stationary Nonseasonal Models. 4. Nonstationary Nonseasonal Models. Part 3 Model construction: 5. Model Identification. 6. Parameter Estimation. 7. Diagnostic Checking. Part 4 Forecasting and simulation: 8. Forecasting with Nonseasonal Models. 9. Simulating with Nonseasonal Models. Part 5 Long memory modelling: 10. The Hurst Phenomenon and Fractional Gaussian Noise. It. Fractional Autoregressive-Moving Average Models. Part 6 Seasonal models: 12. Seasonal Autoregressive Integrated Moving Average Models. 13. Deseasonalized Models. 14. Periodic Models. 15. Forecasting with Seasonal Models. Part 7 Multiple input-single output models: 16. Causality. 17. Constructing Transfer Function-Noise Models. 18. Forecasting with Transfer Function-Noise Models. Part 8 Intervention analysis: 19. Building Intervention Models. Part 9 Multiple input-multiple output models: 20. General Multivariate Autoregressive Moving Average Models. 21. Contemporaneous Autoregressive-Moving Average Models. Part 10 Handling messy environmental data: 22. Exploratory Data Analysis and Intervention Modelling in Confirmatory Data Analysis. 23. Nonparametric Tests for Trend Detection. 24. Regression Analysis and Trend Assessment.

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