timeframes 1.2.1

Lightweight validation framework for time series forecasting.

A lightweight framework for time series validation, backtesting, and visualization.
Timeframes provides simple and efficient tools for splitting, validating, and evaluating 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

# Load example data
df = ts.load_example("air_passengers")  # or "ts_components", "ts_components_complex"

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

# Validate using walk-forward cross-validation
model = LinearRegression()
report, (y_true, y_pred) = ts.validate(
    model, df, target_col="AirPassengers", method="walkforward", mode="expanding", folds=5, return_preds=True
)

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

# Visualize forecast results
ts.plot_forecast(y_true, y_pred, title="Walk-Forward Forecast", show_residuals=True)

💡 You can list all built-in datasets with ts.list_examples().

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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().
• Visual — built-in plot_forecast() with datetime alignment and residuals.
• 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

Timeframes includes several runnable demonstrations for validation, backtesting, and visualization:

Script	Description
examples/forecasts.py	Full workflow: validation + backtest + plots
examples/kfold_demo.py	Temporal K-Fold cross-validation
examples/visualize_splits_demo.py	Visualize expanding, moving, and K-Fold splits

Run any example directly:

python examples/forecasts.py

Or run all __main__ demos automatically (via pytest):

pytest

This executes every example and internal demonstration, ensuring reproducibility across releases.

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🧠 Built-in Datasets

Name	Description	Period	Source
air_passengers	Classic airline passenger dataset	1949–1960	Public domain (Box & Jenkins, 1976)
ts_components	Synthetic trend + seasonality decomposition	2000–2011	Generated (Andrew R. Garcia, 2025)
ts_components_complex	Nonlinear trend + multi-frequency seasonality	2000–2011	Generated (Andrew R. Garcia, 2025)

Load with:

df = ts.load_example("ts_components")

List all available datasets:

ts.list_examples()

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📈 Visualization Example

ts.plot_forecast(
    y_true, 
    y_pred, 
    title="Out-of-Sample Forecast", 
    show_residuals=True
)

Generates a clean, publication-ready figure with automatic datetime indexing and optional residual bars.

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License

MIT License © 2025 Andrew R. Garcia