pattern-causality 0.0.3

Pattern Causality Algorithm in Python

pattern_causality_py

Overview

pattern_causality is a comprehensive Python library that implements the Pattern Causality algorithm for analyzing causal relationships in time series data. This package provides efficient tools for detecting and quantifying causality patterns between multiple time series, with a particular focus on nonlinear complex systems.

Key Features

• Efficient Causality Detection: Robust analysis of causal relationships between pairs of time series
• Parameter Optimization: Automated identification of optimal embedding parameters
• Cross-validation Support: Statistical validation of causality results
• Matrix Analysis: Comprehensive computation of causality matrices for multiple time series
• Effect Analysis: Sophisticated tools for analyzing causal effects in complex systems

Installation

Via pip (Recommended)

pip install pattern-causality

From Source

For the latest development version:

pip install git+https://github.com/skstavroglou/pattern_causality_py.git

Usage Guide

Loading Data

The package includes a pre-processed climate indices dataset for demonstration:

from pattern_causality import load_data

# Load the included climate indices dataset
data = load_data()
print("Available climate indices:", data.columns.tolist())

Basic Causality Analysis

Perform causality analysis between two time series:

from pattern_causality import pc_lightweight

# Prepare data
data = load_data()
X = data['NAO'].values  # North Atlantic Oscillation
Y = data['AAO'].values  # Arctic Oscillation

# Perform pattern causality analysis
result = pc_lightweight(
    X=X, 
    Y=Y, 
    E=3,          # embedding dimension
    tau=1,        # time delay
    h=1,          # prediction horizon
    metric="euclidean",  # distance metric
    weighted=True        # use weighted causality
)
print("Causality Analysis Results:\n", result)

The weighted parameter determines the causality strength calculation method:

• weighted=True: Utilizes the error function (erf) to normalize causality strength
• weighted=False: Uses binary causality strength (1 for presence, 0 for absence)

Parameter Optimization

Identify optimal parameters for your dataset:

from pattern_causality import optimal_parameters_search

data = load_data()
result = optimal_parameters_search(
    Emax=5,       # maximum embedding dimension
    tau_max=5,    # maximum time delay
    metric="euclidean",
    dataset=data.drop(columns=['Date'])
)
print("Optimal Parameters:\n", result)

Cross-Validation Analysis

Validate causality results across different sample sizes:

from pattern_causality import pc_cross_validation

result = pc_cross_validation(
    X=data['NAO'].values,
    Y=data['AAO'].values,
    E=3,
    tau=1,
    metric="euclidean",
    h=1,
    weighted=True,
    numberset=[100, 200, 300, 400, 500]  # sample sizes
)
print("Cross-validation Results:\n", result)

Multi-Series Analysis

Analyze causality patterns across multiple time series:

from pattern_causality import pc_matrix

results = pc_matrix(
    dataset=data.drop(columns=['Date']),
    E=3,
    tau=1,
    metric="euclidean",
    h=1,
    weighted=True
)

print("Pattern Causality Matrix Results:")
print("Positive causality matrix:", results['positive'])
print("Negative causality matrix:", results['negative'])
print("Dark causality matrix:", results['dark'])
print("Variable names:", results['items'])

Effect Analysis

Analyze causal effects between time series:

from pattern_causality import pc_matrix, pc_effect

# Calculate causality matrix
matrix_results = pc_matrix(
    dataset=data.drop(columns=['Date']),
    E=3,
    tau=1,
    metric="euclidean",
    h=1,
    weighted=True
)

# Analyze effects
effects = pc_effect(matrix_results)
print("Causal Effects Analysis:")
print("Positive effects:", effects['positive'])
print("Negative effects:", effects['negative'])
print("Dark effects:", effects['dark'])

Testing

The package includes comprehensive test coverage:

# Install test dependencies
pip install pytest pytest-cov

# Run tests
python -m pytest tests/

# Run tests with coverage report
python -m pytest tests/ --cov=pattern_causality

Current test coverage: 79%

Contributing

We welcome contributions! Please follow these steps:

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Submit a pull request

For major changes, please open an issue first to discuss proposed modifications.

Development Setup

1. Clone the repository
2. Install development dependencies:

   pip install -e ".[dev]"
   

3. Run tests:

   pytest
   

References

• Stavroglou, S. K., Pantelous, A. A., Stanley, H. E., & Zuev, K. M. (2019). Hidden interactions in financial markets. Proceedings of the National Academy of Sciences, 116(22), 10646-10651.

• Stavroglou, S. K., Pantelous, A. A., Stanley, H. E., & Zuev, K. M. (2020). Unveiling causal interactions in complex systems. Proceedings of the National Academy of Sciences, 117(14), 7599-7605.

• Stavroglou, S. K., Ayyub, B. M., Kallinterakis, V., Pantelous, A. A., & Stanley, H. E. (2021). A novel causal risk‐based decision‐making methodology: The case of coronavirus. Risk Analysis, 41(5), 814-830.

License

This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.