What is Yohou-Optuna?

Yohou-Optuna brings Optuna’s hyperparameter optimization to Yohou, providing a Yohou-compatible search class for time series forecasting.

This integration replaces grid and random search with adaptive sampling (TPE, CMA-ES, and more) while keeping Yohou’s forecasting API intact. After fitting, OptunaSearchCV behaves like a Yohou forecaster, so you can call predict, observe, and observe_predict directly.

It integrates with Optuna’s distributions, samplers, and storages, and wraps them for sklearn-style cloning and serialization.

What are the features of Yohou-Optuna?

• Adaptive optimization: Run Optuna studies over Yohou forecasters with TPE, CMA-ES, and other samplers to find better configurations in fewer trials.
• Forecaster-native API: OptunaSearchCV is a forecaster after fitting, so you can call predict, observe, observe_predict, and interval methods.
• Clone-safe wrappers: Sampler, Storage, Callback, and Pruner wrappers ensure Optuna objects survive sklearn cloning and serialization.
• Fold-level pruning: Pass a Pruner to stop a trial…