The Complex Systems Approach
Course Guide
The Complex Systems Approach
I Introduction
1 A Quick Guide to Scientific Rigour
2 Introduction to Complexity Methods
II Mathematics of Change
3 Introduction to the Mathematics of Change
III Basic (Nonlinear) Time Series Analysis
4 Basic Time Series Analysis
5 Basic Nonlinear Time Series Analysis
IV Scaling Phenomena - Fluctuation Analyses
6 Fluctuation Analyses: Global Scaling
7 Fluctuation Analyses: Local Scaling
V Recurrence Based Analyses
8 Recurrence Quantification Analysis
9 Unordered Categorical Data
- 9.1 Categorical Auto-RQA
  - 9.1.1 Auto-RQA Measures
  - 9.1.2 The theiler window
- 9.2 Categorical Cross-RQA
10 Continuous Data
11 An R interface to Marwan’s commandline recurrence plots
12 Computational load: The Python solution [PyRQA]
- Setup the environment
VI Multivariate Timeseries Analysis
13 Vector Auto Regression (VAR)
14 Dynamic Complexity
- Study Materials and Resources
  - Systems Innovation
15 Graph Theory and Complex Network Analysis
APPENDICES
A Some Notes on Using R
B Working with time series in R
C Dealing with Missing Data
D List of terms
References
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The Complex Systems Approach to Behavioural Science

Schedule

You can find detailed schedules on the course website: https://complexity-methods.github.io/courses/