Comprehensive coursework covering agent-based modeling, cellular automata simulations, and time series analysis with practical implementations in Python and Excel.
This repository contains projects and coursework from Complex Computational Models (CSC1111/CSC1139), focusing on simulation techniques, time series forecasting, and computational modeling of complex systems.
Course Codes: CSC1111, CSC1139 Topics: Cellular Automata, Agent-Based Models, Time Series Analysis Status: ✅ Completed with Multiple Projects
- Understand and implement cellular automata and agent-based models
- Master time series analysis and forecasting techniques
- Apply computational modeling to real-world phenomena
- Develop simulation skills for complex adaptive systems
- Analyze and visualize emergent behaviors
Complex Comp Models/
├── Cellular Automata/
│ ├── Lab01.ipynb # Introduction to cellular automata
│ ├── agent_based_model.py # General ABM framework
│ ├── predator_prey_simulation.py # Predator-prey dynamics
│ ├── segregation_model.py # Schelling's segregation model
│ ├── dla_simulation.py # Diffusion-limited aggregation
│ ├── network_visualization.py # Network analysis and viz
│ └── assignment/
│ ├── virus_spread_simulation.py # COVID-19 spread simulation
│ ├── report_generator.py # Automated report creation
│ └── README.md # Assignment documentation
│
├── Time Series/
│ ├── W1/ - W6/ # Weekly materials
│ ├── TSClassExercise_answers.xlsx # Time series class exercises
│ ├── MA_Forecast_model_answers.xlsx # Moving Average models
│ ├── School_leavers_additive_model.xlsx # Additive decomposition
│ ├── JandJ_time_series_solutions.xlsx # Johnson & Johnson analysis
│ └── VIX_daily_analysis.xlsx # Volatility index analysis
│
├── Past Exams/
│ └── [Exam papers and solutions]
│
├── Lean_Business_Model_Canvas_GreenGrid_AI.pdf
├── Cheat_Sheet.pdf
└── revision_notes.md
A sophisticated agent-based model simulating disease spread through a population.
Features:
- Configurable infection parameters (transmission rate, recovery time, mortality)
- Spatial movement and contact tracing
- Intervention strategies (lockdowns, social distancing, vaccination)
- Real-time visualization of spread dynamics
- Statistical analysis of outbreak patterns
Skills: Agent-based modeling, simulation, Python OOP, data visualization
Files: Cellular Automata/assignment/virus_spread_simulation.py
Implementation of Lotka-Volterra equations and agent-based predator-prey models.
Features:
- Population dynamics simulation
- Emergent cyclical patterns
- Parameter sensitivity analysis
- Visualization of population trajectories
Files: Cellular Automata/predator_prey_simulation.py
Simulation demonstrating how individual preferences can lead to large-scale segregation.
Features:
- Agent preferences and movement rules
- Emergent segregation patterns
- Threshold analysis
- Spatial visualization
Files: Cellular Automata/segregation_model.py
Fractal growth simulation modeling particle aggregation.
Features:
- Random walk particle dynamics
- Fractal pattern formation
- Visualization of growth structures
- Dimension analysis
Files: Cellular Automata/dla_simulation.py
Complex network analysis and visualization tools.
Features:
- Graph generation and analysis
- Network metrics (centrality, clustering)
- Community detection
- Interactive visualizations
Files: Cellular Automata/network_visualization.py
Comprehensive exercises covering fundamental time series concepts.
Topics Covered:
- Trend identification and removal
- Seasonal decomposition
- Stationarity testing
- Autocorrelation analysis
Files: Time Series/TSClassExercise_answers.xlsx
Implementation and comparison of MA forecasting techniques.
Models:
- Simple Moving Average (SMA)
- Weighted Moving Average (WMA)
- Exponential Smoothing
- Model performance comparison
Files: Time Series/MA_Forecast_model_answers.xlsx
Seasonal decomposition using additive model.
Analysis:
- Trend-cycle component extraction
- Seasonal pattern identification
- Irregular component analysis
- Forecasting with seasonal adjustment
Files: Time Series/School_leavers_additive_model.xlsx
Real-world financial time series analysis.
Techniques:
- Multiplicative decomposition
- Exponential growth trends
- Seasonal indices calculation
- Business forecasting
Files: Time Series/JandJ_time_series_solutions.xlsx
Volatility Index (VIX) time series analysis.
Focus:
- Financial volatility modeling
- ARCH/GARCH concepts
- Market risk analysis
- Volatility forecasting
Files: Time Series/VIX_daily_analysis.xlsx
numpy==1.24.0 # Numerical computing
matplotlib==3.7.0 # Visualization
scipy==1.10.0 # Scientific computing
networkx==3.0 # Network analysis
pandas==2.0.0 # Time series data manipulation- Custom agent-based modeling framework
- Cellular automata implementations
- Random walk simulations
- Microsoft Excel (time series analysis)
- Jupyter Notebook (interactive development)
- Python statistical libraries
- Conway's Game of Life
- Elementary cellular automata (Rule 30, Rule 110)
- 2D grid-based simulations
- Emergence and self-organization
- Computational universality
- Individual agent behaviors
- Interaction rules
- Emergent collective phenomena
- Spatial dynamics
- Parameter sensitivity
- Model validation
- Components: Trend, Seasonal, Cyclical, Irregular
- Decomposition methods (additive/multiplicative)
- Smoothing techniques
- Forecasting methods:
- Moving Averages
- Exponential Smoothing
- Holt-Winters
- ARIMA concepts
- Model evaluation (MAE, RMSE, MAPE)
- Nonlinearity and feedback loops
- Self-organization
- Emergence
- Power laws and scale-free networks
- Tipping points and phase transitions
Develop an agent-based model to simulate disease spread and evaluate intervention strategies.
- Population size: Configurable
- Infection rate: Beta parameter
- Recovery time: Average days to recovery
- Mortality rate: Percentage of infected who die
- Movement: Random walk or directed
- Interventions: Lockdown, social distancing, vaccination
- ✅ Python simulation code
- ✅ Visualization of spread dynamics
- ✅ Statistical analysis
- ✅ Report on intervention effectiveness
- ✅ Automated report generation
- Successfully modeled disease spread dynamics
- Demonstrated effectiveness of early interventions
- Analyzed parameter sensitivity
- Generated reproducible results
Documentation: See Cellular Automata/assignment/README.md
Objective: Forecast number of school leavers using seasonal decomposition Method: Additive model Result: Accurate seasonal forecasts with trend analysis
Objective: Analyze quarterly earnings and forecast future performance Method: Multiplicative decomposition with exponential trend Result: Successfully captured growth trend and seasonal patterns
Objective: Model and forecast market volatility Method: Advanced time series techniques Result: Insights into market risk patterns
Objective: Compare forecasting performance of different MA methods Method: SMA, WMA, Exponential Smoothing Result: Exponential smoothing performed best for trending data
Python 3.8 or higher
Microsoft Excel (for time series exercises)
Jupyter Notebook (optional)- Navigate to project directory
cd "D:\Final Year\Complex Comp Models"- Install Python dependencies
pip install numpy matplotlib scipy pandas networkx
pip install jupyter notebook # Optional- Run simulations
# Virus spread simulation
python "Cellular Automata/assignment/virus_spread_simulation.py"
# Predator-prey model
python "Cellular Automata/predator_prey_simulation.py"
# Segregation model
python "Cellular Automata/segregation_model.py"- Open time series exercises
- Open Excel files in
Time Series/folder - Follow worksheet instructions
- Review solutions and methodologies
- Wolfram: "A New Kind of Science"
- Railsback & Grimm: "Agent-Based and Individual-Based Modeling"
- Box & Jenkins: "Time Series Analysis: Forecasting and Control"
- Hyndman & Athanasopoulos: "Forecasting: Principles and Practice"
- Weekly lecture notes (W1-W6)
- Past exam papers with solutions
- Cheat sheet for quick reference
- Revision notes
- Agent-Based Modeling
- Cellular Automata
- Discrete Event Simulation
- Monte Carlo Methods
- Stochastic Processes
- Decomposition Techniques
- Forecasting Methods
- Trend Analysis
- Seasonal Adjustment
- Model Selection and Evaluation
- Python OOP Design
- NumPy and SciPy
- Matplotlib Visualization
- Network Analysis (NetworkX)
- Excel Data Analysis
- Complex Systems Understanding
- Emergence Recognition
- Parameter Sensitivity Analysis
- Model Validation
- Critical Evaluation
- Power law distributions
- Scale-free networks
- Self-organized criticality
- Chaos theory basics
- Adaptive complex systems
- Machine learning integration with ABM
- Real-time simulation visualization
- GPU-accelerated simulations
- Deep learning for time series (LSTM)
- Multi-agent reinforcement learning
Application of complex models to sustainability startup:
- Problem: Energy grid inefficiency
- Solution: Agent-based modeling for smart grid optimization
- Key Metrics: Energy savings, grid stability
- Competitive Advantage: Predictive modeling using complex systems
File: Lean_Business_Model_Canvas_GreenGrid_AI.pdf
- Virus Spread Simulation: [Grade]
- Time Series Forecasting: [Grade]
- Cellular Automata Implementation: [Grade]
- Midterm: [Score]
- Final: [Score]
Demonstrated strong understanding of:
- Simulation methodology
- Time series techniques
- Complex systems theory
- Python implementation
- Analytical problem-solving
Academic coursework - All rights reserved
- Course instructors for comprehensive teaching
- Open-source simulation communities
- Research papers on complex systems
- Textbook authors
Course: CSC1111/CSC1139 - Complex Computational Models Academic Year: 2025-2026 Topics: Agent-Based Modeling, Cellular Automata, Time Series Last Updated: June 2026