greybox 1.0.0

Toolbox for model building and forecasting

greybox

Python port of the R greybox package — a toolbox for regression model building and forecasting.

Installation

pip install greybox

For more installation options, see the Installation wiki page.

Quick Example

import numpy as np
import pandas as pd
from greybox import ALM, formula

# Generate sample data
np.random.seed(42)
n = 200
data = pd.DataFrame({
    "y": np.random.normal(10, 2, n),
    "x1": np.random.normal(5, 1, n),
    "x2": np.random.normal(3, 1, n),
})
data["y"] = 2 + 0.5 * data["x1"] - 0.3 * data["x2"] + np.random.normal(0, 1, n)

# Parse formula and fit model
result = formula("y ~ x1 + x2", data=data)
model = ALM(distribution="dnorm")
model.fit(result.data, result.formula)

# Summary
print(model.summary())

# Predict with intervals
pred = model.predict(result.data, interval="prediction", level=0.95)
print(pred.mean[:5])

# Include AR terms (ARIMA-like models)
# For example, ARIMA(1,1,0) model with Log-Normal distribution:
model = ALM(distribution="dlnorm", orders=(1, 1, 0))
model.fit(X, y)

Supported Distributions

Category	Distributions
Continuous	dnorm, dlaplace, ds, dgnorm, dlgnorm, dfnorm, drectnorm, dt
Positive	dlnorm, dinvgauss, dgamma, dexp, dchisq
Count	dpois, dnbinom, dgeom
Bounded	dbeta, dlogitnorm, dbcnorm
CDF-based	pnorm, plogis
Other	dalaplace, dbinom

Features

• ALM (Augmented Linear Model): Likelihood-based regression with 26 distributions
• Formula parser: R-style formulas (y ~ x1 + x2, log(y) ~ ., y ~ 0 + x1) with support for backshift operator
• stepwise(): IC-based variable selection with partial correlations
• CALM(): Combine ALM models based on IC weights
• Forecast error measures: MAE, MSE, RMSE, MAPE, MASE, MPE, sMAPE, and more
• Variable processing: xreg_expander (lags/leads), xreg_multiplier (interactions), temporal_dummy
• Distributions: 27 distribution families with density, CDF, quantile, and random generation
• Association: Partial correlations and measures of association
• Diagnostics: Model diagnostics and validation

Links

• Wiki documentation
• GitHub repository
• R package on CRAN

License

LGPL-2.1