Acknowledgements
Contents
Abbreviations
1 Introduction
References
2 Analysis of Scalar Time Series
2.1 Introduction
2.2 Preliminary Processing
2.2.1 No Preliminary Processing Required
2.2.2 Linear Trend
2.2.3 The Hopping Averaging
2.2.4 Seasonal Trend Removal
2.2.5 Linear Filtering
2.3 Time Domain Analysis
2.4 Frequency Domain Analysis
2.5 Statistical Predictability and Prediction
2.6 Verification of GCM-Simulated Climate. The Scalar Case
2.7 Engineering Time Series
2.8 Conclusions
Attachment 2.1: Weights and Frequency Response Functions of Linear Filters
Attachment 2.2: Examples of Optimal Nonlinear Extrapolation of Stationary Random Processes
Introduction
Continuous Markov Random Processes
Disconnected Random Processes
References
3 Bivariate Time Series Analysis
3.1 Introduction
3.2 Products of Bivariate Time Series Analysis with AVESTA3
3.3 Finding Dependence Between Time Series with AVESTA3
3.4 Teleconnection Between Global Temperature and ENSO
3.5 Time Series Reconstruction
3.6 Verification of GCM-Simulated Climate. The Bivariate Case
3.7 Bivariate Analysis of Mechanical Engineering Time Series
3.8 Conclusions
References
4 Analysis of Trivariate Time Series
4.1 Products of Trivariate Time Series Analysis with AVESTA3
4.2 Application to Geophysical Data
4.3 Analysis of Global, Hemispheric, Oceanic, and Terrestrial Data Sets
4.4 Application to Engineering Data
4.5 Conclusions
References
5 Conclusions and Recommendations
References