Python port of the R greybox package — a toolbox for regression model building and forecasting.
pip install greyboxFor more installation options, see the Installation wiki page.
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
y, X = formula("y ~ x1 + x2", data=data)
model = ALM(distribution="dnorm")
model.fit(X, y)
# 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)| 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 |
- 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