prism-engines 0.1.0

Geometric analysis engines for time series data

PRISM Engines

Geometric analysis engines for time series data.

Installation

pip install prism-engines

For plotting support:

pip install prism-engines[plot]

Quick Start

import prism_engines as prism

# Load CSV and run all engines
results = prism.run("your_data.csv")

# Print analysis report
results.print_report()

# Generate visualization
results.plot()

# Save results
results.save("output/", name="my_analysis")

What It Does

PRISM Engines analyzes multi-variate time series to reveal geometric structure:

Engine	What It Measures
Correlation	Pairwise relationships between series
PCA	Dimensionality and dominant modes
Hurst	Memory/persistence in each series

Example Output

============================================================
PRISM ENGINES ANALYSIS REPORT
============================================================

Data: (100, 5) (5 series)
Range: 2024-01-01 to 2024-04-10

--- CORRELATION ---
  mean_abs_correlation: 0.4523
  max_correlation: {'pair': ('AAPL', 'MSFT'), 'value': 0.892}

--- PCA ---
  explained_variance_ratio: [0.523, 0.218, 0.142, 0.089, 0.028]
  effective_dimension: 3
  global_forcing_metric: 0.523

--- HURST ---
  mean_hurst: 0.612
  persistence_classification: {'AAPL': 'persistent', ...}
============================================================

Detailed Usage

Loading Data

from prism_engines import load_csv

# Load and validate CSV
df = load_csv("data.csv")

Your CSV should have:

• First column: dates (will become index)
• Remaining columns: numeric time series

Running Specific Engines

from prism_engines import run_engines

# Run only PCA and correlation
results = run_engines(df, engines=["pca", "correlation"])

Accessing Results

# Get specific engine result
pca = results["pca"]

# Access metrics
print(pca.metrics["global_forcing_metric"])
print(pca.metrics["effective_dimension"])

# Check all available metrics
print(pca.metrics.keys())

Available Engines

from prism_engines import list_engines

print(list_engines())
# ['correlation', 'pca', 'hurst']

Metrics Reference

Correlation Engine

• correlation_matrix: NxN correlation matrix
• mean_abs_correlation: Average |correlation|
• max_correlation: Strongest correlated pair
• min_correlation: Weakest correlated pair

PCA Engine

• explained_variance_ratio: Variance per component
• effective_dimension: Components for 90% variance
• global_forcing_metric: PC1 dominance (higher = more shared movement)
• pc1_loadings: How each series loads on PC1

Hurst Engine

• hurst_exponents: H value per series
• mean_hurst: Average H
• persistence_classification: Category per series
  • H < 0.4: anti-persistent (mean-reverting)
  • 0.4 ≤ H ≤ 0.6: random walk
  • H > 0.6: persistent (trending)

License

MIT