randomstatsmodels 1.1.1

Tools for benchmarking, metrics, and models.

randomstatsmodels

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Lightweight utilities for benchmarking, forecasting, and statistical modeling — with simple Auto* model wrappers that tune hyperparameters for you.

Installation

pip install randomstatsmodels

Requires: Python 3.9+ and NumPy.

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Quick Start

from randomstatsmodels import AutoNEO, AutoFourier, AutoKNN, AutoPolymath, AutoThetaAR
import numpy as np

# Toy data: sine wave + noise
rng = np.random.default_rng(42)
t = np.arange(200)
y = np.sin(2*np.pi*t/24) + 0.1*rng.normal(size=t.size)

h = 12  # forecast horizon

model = AutoNEO().fit(y)
yhat = model.predict(h)
print("Forecast:", yhat[:5])

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Models

Each Auto* class:

• accepts a parameter grid (or uses sensible defaults),
• fits/evaluates candidates using a chosen metric,
• exposes a unified API: .fit(y[, X]) and .predict(h).

AutoNEO

from randomstatsmodels import AutoNEO

neo = AutoNEO(
    param_grid={"n_components": [8, 16, 32]},
    metric="mae",
)
neo.fit(y)
print("Best params:", neo.best_params_)
print("Prediction:", neo.predict(h))

AutoFourier

from randomstatsmodels import AutoFourier

fourier = AutoFourier(
    param_grid={"season_length": [12, 24], "n_terms": [3, 5]},
    metric="smape",
)
fourier.fit(y)
print("Prediction:", fourier.predict(h))

AutoKNN

from randomstatsmodels import AutoKNN

knn = AutoKNN(
    param_grid={"k": [3, 5, 7], "window": [12, 24]},
    metric="rmse",
)
knn.fit(y)
print("Prediction:", knn.predict(h))

AutoPolymath

from randomstatsmodels import AutoPolymath

poly = AutoPolymath(
    param_grid={"degree": [2, 3], "ridge": [0.0, 0.1]},
    metric="mae",
)
poly.fit(y)
print("Prediction:", poly.predict(h))

AutoThetaAR

from randomstatsmodels import AutoThetaAR

theta = AutoThetaAR(
    param_grid={"theta": [0.5, 1.0, 2.0]},
    metric="mape",
)
theta.fit(y)
print("Prediction:", theta.predict(h))

AutoHybridForecaster

from randomstatsmodels import AutoHybridForecaster

hybrid = AutoHybridForecaster(
    candidate_fourier=(0, 3, 6),
    candidate_trend=(0, 1),
    candidate_ar=(0, 3, 5),
    candidate_hidden=(8, 16, 32),
)
hybrid.fit(y)
print("Best config:", hybrid.best_config)
print("Prediction:", hybrid.predict(h))

AutoMELD

from randomstatsmodels import AutoMELD

meld = AutoMELD(
    lags_grid=(8, 12),
    scales_grid=((1, 3, 7), (1, 2, 4, 8)),
    rff_features_grid=(64, 128),
)
meld.fit(y)
print("Best config:", meld.best_["config"])
print("Prediction:", meld.predict(h))

AutoPALF

from randomstatsmodels import AutoPALF

palf = AutoPALF(
    p_candidates=(4, 8, 12),
    penalties=("huber", "l2"),
)
palf.fit(y)
print("Validation score:", palf.best_["val_score"])
print("Prediction:", palf.predict(h))

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Metrics

Available out of the box:

from randomstatsmodels.metrics import mae, mse, rmse, mape, smape

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License

MIT © 2025 Jacob Wright