feat: reuse shared panel processing across forecasters (refresh of #2)

[elmartinj]

This is a refreshed version of #2 (feat: use common syntaxis and improve speed), rebased onto current main. The original commits by Azul are preserved on this branch; the final commit fixes what was blocking the old PR.

What's in it:

• Preprocess the input DataFrame once into a shared PanelData and reuse it across model calls instead of every wrapper re-doing sorting, datetime conversion, and dataset construction.
• Thread the optional panel through the base Forecaster.forecast signature and MultiModelForecasterMixin.forecast, and update every built-in model wrapper (including Tafsut and TimesFM 3.0, which landed on main after the original PR).
• Fix the test failures from feat: use common syntaxis and improve speed #2's last CI run: quantile columns are now assigned one at a time because utilsforecast.assign_columns rejects list values for multiple columns.
• Compute the Chronos panel once so dataset construction and finetuning inputs share it.
• Remove redundant _ = panel statements in the TimesFM 1.0 / 2.5 paths.

Local verification: pre-commit (ruff, ruff-format, mypy, toml-sort) passes and the download-free test suite passes (78 passed, 6 skipped). Full model tests still need a CI run.

Related: #2

[CLAassistant]

Thank you for your submission! We really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.
1 out of 2 committers have signed the CLA.

✅ AzulGarza
❌ elmartinj

---

elmartinj seems not to be a GitHub user. You need a GitHub account to be able to sign the CLA. If you have already a GitHub account, please add the email address used for this commit to your account.
_{You have signed the CLA already but the status is still pending? Let us recheck it.}

[codspeed-hq]

Merging this PR will improve performance by ×3

⚠️ Different runtime environments detected

Some benchmarks with significant performance changes were compared across different runtime environments,
which may affect the accuracy of the results.

Open the report in CodSpeed to investigate

⚡ 4 improved benchmarks
✅ 7 untouched benchmarks
🆕 6 new benchmarks

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
⚡	Simulation	test_foundation_forecast_multi_model[5-models]	965.4 ms	173.2 ms	×5.6
⚡	Simulation	test_foundation_forecast_multi_model[3-models]	578.4 ms	145.8 ms	×4
⚡	Simulation	test_timeseries_dataset_from_df[small]	10.8 ms	5.2 ms	×2.1
⚡	Simulation	test_timeseries_dataset_from_df[large]	180.9 ms	107.4 ms	+68.4%
🆕	Simulation	test_assign_quantile_forecasts	N/A	4.6 ms	N/A
🆕	Simulation	test_process_panel_from_df_string_ds	N/A	225.1 ms	N/A
🆕	Simulation	test_process_panel_from_df[large]	N/A	104.5 ms	N/A
🆕	Simulation	test_process_panel_from_df[small]	N/A	4.7 ms	N/A
🆕	Simulation	test_timeseries_dataset_from_df_with_panel	N/A	41.4 µs	N/A
🆕	Simulation	test_timeseries_dataset_from_panel	N/A	34.2 µs	N/A

Tip

Curious why performance improved? Comment @codspeedbot explain why performance improved on this PR, or directly use the CodSpeed MCP with your agent.

---

_{Comparing feat/improve-speed-refresh (35c8f31) with main (cbc64bc)}