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Integrative Historical Prediction (IHP) Framework

Overview

The Integrative Historical Prediction (IHP) framework is a novel approach to analyzing and forecasting complex societal phenomena. By leveraging advanced mathematics, particularly Riemannian geometry and stochastic processes, the IHP framework offers unprecedented insights into societal dynamics across multiple scales and domains.

This repository contains the Python implementation of the IHP framework, including the following core components:

  1. Event Continuum Mapping (ECM) system
  2. Societal Dynamics Engine (SDE)
  3. Predictive Modeling Algorithm (PMA)
  4. Multiscale Temporal Convolutional Network (MTCN)

The framework is demonstrated through three case studies:

  1. Retrospective analysis of the Arab Spring
  2. Near-future predictions of global cryptocurrency adoption
  3. Long-term forecasting of climate-induced migration patterns

For a graphical overview, see https://excalidraw.com/#json=Z-np5KMmzWodTlvTX85fK,VVWcrW3OMmhvxoHYrGKG2Q

Installation

To install the IHP framework and its dependencies, follow these steps:

  1. Clone the repository:

    git clone https://github.com/your-username/ihp-framework.git
cd ihp-framework

  2. Create a virtual environment and activate it:

    python -m venv venv
source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

  3. Install the required dependencies:

    pip install -r requirements.txt

  4. Realize this is a work of fiction, inspired by Tolstoy and Asimov

Usage

Do not use the IHP framework for analysis or prediction: it is a work of fiction -- see WHAT⁉️.md. But for the continued fictional notion that this was a real project here are the fictional usage steps:

  1. Import the necessary modules:

    from ihp_framework import ECM, SDE, PMA, MTCN

  2. Initialize the components:

    ecm = ECM(manifold_dim=100)
sde = SDE(manifold_dim=100, num_variables=50)
pma = PMA(input_dim=100, output_dim=10, num_layers=8, kernel_size=3, num_filters=64, dropout_rate=0.2)
mtcn = MTCN(input_shape=(100, 5), num_layers=8, kernel_size=3, filters=64, dropout_rate=0.2, output_dim=1)

  3. Load and preprocess your data:

    data = load_and_preprocess_data()

  4. Perform analysis and generate predictions:

    ecm_results = ecm.analyze_event_patterns(data)
sde_results = sde.simulate_dynamics(data)
predictions = pma.predict(data)
mtcn_results = mtcn.fit_and_predict(data)

  5. Visualize and interpret the results:

    visualize_results(ecm_results, sde_results, predictions, mtcn_results)

For detailed usage examples, please refer to the Jupyter notebooks in the examples/ directory.

Case Studies

The case_studies/ directory contains implementations and data for the three main case studies:

  1. arab_spring/: Retrospective analysis of the Arab Spring events
  2. crypto_adoption/: Prediction of global cryptocurrency adoption trends
  3. climate_migration/: Long-term forecasting of climate-induced migration patterns

Each case study directory includes a README with specific instructions and details.

Contributing

Contributions to the IHP framework are welcome! Please read our CONTRIBUTING.md file for guidelines on how to submit issues, feature requests, and pull requests.

License

This fictional project is licensed under the MIT License. See the LICENSE file for details.

Citation

If you use the IHP framework in your research, please reconsider. It is a work of fiction. But for completion of the fiction, cite the original paper:

Austegard, O., & Sonnett, C. (2024). Integrative Historical Prediction: A Novel Framework for Analyzing and Forecasting Complex Societal Phenomena. Journal of Complex Systems Analysis, 15(2), 123-456.

Contact

For questions or feedback, you can attempt (and fail to) contact:

Project Link: https://github.com/oaustegard/ihp-framework