[ENH] extend `sktime` `ForecastingOptCV` with broadcasting options and returned parameters

[anirudhsengar]

Summary

Extend ForecastingOptCV to expose sktime’s broadcasting controls plus richer post-fit diagnostics, aligning it more closely with ForecastingGridSearchCV while keeping Hyperactive optimizers in play. Also document the new knobs and assert the behavior via integration tests.

Changes

• Broadcasting flags: Added tune_by_instance/tune_by_variable init params. When sktime is available we reuse its delegated tagging (_set_delegated_tags + _extend_to_all_scitypes) so panel/multivariate broadcasting follows the native implementation rather than a bespoke copy.
• Returned attributes: fit now records scorer_, n_splits_, best_index_, best_score_, best_params_, best_forecaster_, refit_time_, and a cv_results_ table (the backtesting results for the winning configuration). We time refits with time.perf_counter and keep the optimizer’s best index when available.
• Docs & tests: README.md explains the new parameters/diagnostics. test_sktime_estimators.py gained regression tests to ensure the attributes populate and the tune-by flags adjust estimator tags.

Related Issues

Fixes https://github.com/SimonBlanke/Hyperactive/issues/198

[anirudhsengar]

Done! Thank you @fkiraly for the feedback.

[anirudhsengar]

If approved, please merge https://github.com/sktime/sktime/pull/9092 as well.

[anirudhsengar]

Done! @fkiraly. Even updated https://github.com/sktime/sktime/pull/9092.