yohou-nixtla 0.1.0a6

What is Yohou-Nixtla?

Yohou-Nixtla brings the power of Nixtla's forecasting ecosystem to Yohou, providing Yohou-compatible wrappers for statistical, machine learning, and deep learning time series models.

This integration enables you to use Nixtla's high-performance forecasters (StatsForecast, NeuralForecast) within Yohou's unified API for time series forecasting. All models work seamlessly with Yohou's features: polars DataFrames, panel data support, cross-validation, and hyperparameter search via GridSearchCV/RandomizedSearchCV.

What are the features of Yohou-Nixtla?

• Statistical Models: AutoARIMA, AutoETS, AutoTheta, ARIMA, Holt-Winters, Naive, and more from StatsForecast, providing fast, production-ready statistical forecasters.
• Neural Models: NBEATS, NHITS, MLP, PatchTST, TimesNet from NeuralForecast, offering state-of-the-art deep learning architectures.
• Panel Data: Native support for multiple time series with __ column naming convention (e.g., sales__store_1, sales__store_2).
• Yohou Compatible: Full fit/predict, get_params/set_params, clone compatibility. Works with GridSearchCV, pipelines, and the Yohou ecosystem.
• Polars Native: All data handling uses polars DataFrames for high-performance time series operations.

Note: Nixtla's MLForecast is not wrapped as Yohou already provides PointReductionForecaster, which turns any scikit-learn regressor (Ridge, LightGBM, XGBoost, …) into a recursive multi-step forecaster with full support for actual transformers, target transformers, and panel data.

How to install Yohou-Nixtla?

Install the Yohou-Nixtla package using pip:

pip install yohou_nixtla

or using uv:

uv pip install yohou_nixtla

or using conda:

conda install -c conda-forge yohou_nixtla

or using mamba:

mamba install -c conda-forge yohou_nixtla

or alternatively, add yohou_nixtla to your requirements.txt or pyproject.toml file.

How to get started with Yohou-Nixtla?

1. Fit a Statistical Forecaster

Use AutoARIMA for automatic ARIMA model selection:

import polars as pl
from yohou_nixtla import AutoARIMAForecaster

# Load your time series data (must have a "time" column)
y = pl.DataFrame({
    "time": pl.datetime_range(start="2020-01-01", end="2020-12-31", interval="1d", eager=True),
    "sales": [100 + i * 0.5 + (i % 7) * 10 for i in range(366)],
})

# Fit and predict
forecaster = AutoARIMAForecaster(season_length=7)
forecaster.fit(y, forecasting_horizon=14)
y_pred = forecaster.predict()

2. Train Deep Learning Models

Neural models for complex patterns:

from yohou_nixtla import NHITSForecaster

forecaster = NHITSForecaster(input_size=30, max_steps=100)
forecaster.fit(y, forecasting_horizon=14)
y_pred = forecaster.predict()

3. Panel Data Forecasting

Forecast multiple time series simultaneously:

# Panel data with __ separator
y_panel = pl.DataFrame({
    "time": pl.datetime_range(start="2020-01-01", end="2020-12-31", interval="1d", eager=True),
    "sales__store_1": [...],
    "sales__store_2": [...],
})

forecaster = AutoARIMAForecaster(season_length=7)
forecaster.fit(y_panel, forecasting_horizon=14)
y_pred = forecaster.predict()  # Predictions for all stores

How do I use Yohou-Nixtla?

Full documentation is available at https://yohou-nixtla.readthedocs.io/.

Interactive examples are available in the examples/ directory:

• Online: https://yohou-nixtla.readthedocs.io/en/latest/pages/examples/
• Locally: Run marimo edit examples/hello.py to open an interactive notebook

Can I contribute?

We welcome contributions, feedback, and questions:

• Report issues or request features: GitHub Issues
• Join the discussion: GitHub Discussions
• Contributing Guide: CONTRIBUTING.md

If you are interested in becoming a maintainer or taking a more active role, please reach out to Guillaume Tauzin on GitHub Discussions.

Where can I learn more?

Here are the main Yohou-Nixtla resources:

• Full documentation: https://yohou-nixtla.readthedocs.io/
• GitHub Discussions: https://github.com/stateful-y/yohou-nixtla/discussions
• Interactive Examples: https://yohou-nixtla.readthedocs.io/en/latest/pages/examples/

For questions and discussions, you can also open a discussion.

License

This project is licensed under the terms of the Apache-2.0 License.

Acknowledgements

This project is maintained by stateful-y, an ML consultancy specializing in time series data science & engineering. If you're interested in collaborating or learning more about our services, please visit our website.