Book contents
- Frontmatter
- Contents
- Preface
- Notation Used
- Abbreviations
- 1 Introduction
- 2 Basics
- 3 Probability Distributions
- 4 Statistical Inference
- 5 Linear Regression
- 6 Neural Networks
- 7 Non-linear Optimization
- 8 Learning and Generalization
- 9 Principal Components and Canonical Correlation
- 10 Unsupervised Learning
- 11 Time Series
- 12 Classification
- 13 Kernel Methods
- 14 Decision Trees, Random Forests and Boosting
- 15 Deep Learning
- 16 Forecast Verification and Post-processing
- 17 Merging of Machine Learning and Physics
- Appendices
- References
- Index
11 - Time Series
Published online by Cambridge University Press: 23 March 2023
Book contents
- Frontmatter
- Contents
- Preface
- Notation Used
- Abbreviations
- 1 Introduction
- 2 Basics
- 3 Probability Distributions
- 4 Statistical Inference
- 5 Linear Regression
- 6 Neural Networks
- 7 Non-linear Optimization
- 8 Learning and Generalization
- 9 Principal Components and Canonical Correlation
- 10 Unsupervised Learning
- 11 Time Series
- 12 Classification
- 13 Kernel Methods
- 14 Decision Trees, Random Forests and Boosting
- 15 Deep Learning
- 16 Forecast Verification and Post-processing
- 17 Merging of Machine Learning and Physics
- Appendices
- References
- Index
Summary
Under time series analysis, one proceeds from Fourier analysis to the design of windows, then spectral analysis (e.g. computing the spectrum, the cross-spectrum between two time series, wavelets, etc.) and the filtering of frequency signals. The principal component analysis method can be turned into a spectral method known as singular spectrum analysis. Auto-regressive processes and Box-Jenkins models are also covered.
Keywords
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- Introduction to Environmental Data Science , pp. 372 - 417Publisher: Cambridge University PressPrint publication year: 2023