timeframes 1.1.0

Lightweight validation framework for time series forecasting.

Timeframes

A lightweight framework for time series validation and evaluation.
Timeframes provides simple and efficient tools for splitting, validating, and backtesting forecasting models — without unnecessary dependencies or boilerplate.

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🧠 Need help?

Try TimeframesGPT — a specialized assistant trained on the full Timeframes codices.

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Installation

pip install timeframes

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Quick Example

import timeframes as ts
from sklearn.linear_model import LinearRegression

# Example data
X, y = ts.load_example("air_passengers")

# Split into train, validation, and test
train, val, test = ts.split(X, ratios=(0.7, 0.2, 0.1))

# Validate using walk-forward cross-validation
model = LinearRegression()
report = ts.validate(model, X, y, method="walkforward", mode="expanding", folds=5)

print(report)
# {'mae': 0.213, 'rmse': 0.322, 'smape': 3.9}

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Features

• Minimal — depends only on NumPy and pandas.
• Consistent — unified API for all validation methods.
• Flexible — works with any model exposing .fit() / .predict().
• Transparent — every function returns clear, reproducible outputs.

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Supported Methods

Function	Description
ts.split()	Single train/validation/test split
ts.validate()	Cross-validation (walk-forward or temporal K-Fold)
ts.backtest()	Out-of-sample testing
ts.evaluate()	Metric evaluation (MAE, RMSE, sMAPE, rMAE)

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📂 Examples

Hands-on demonstrations are included in the examples/ directory:

• harmonic_forecast.py — walk-forward validation and backtesting demo.
• kfold_demo.py — temporal K-Fold cross-validation example.
• visualize_splits_demo.py — visualize how time series splits evolve.

Run any example directly:

python examples/harmonic_forecast.py

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License

MIT License © 2025 Andrew R. Garcia