Add ChakraTS (YHat Labs) API wrapper and fev-bench results

[shubham-forum]

Issue #, if available: None

Description of changes:

• Add the chakrats_api wrapper and pinned dependencies for ChakraTS, a hosted forecasting model from YHat Labs (https://huggingface.co/yhatlabs/ChakraTS). No weights are distributed; the wrapper reads the API key from YHAT_API_KEY, no credentials are included. This submission is for a closed-source API model as discussed with Oleksandr.

• Add results for all 100 fev-bench tasks in benchmarks/fev_bench/results/chakrats_api.csv: SQL skill score 48.1 (seasonal-naive normalised). The results are for chakra-ts-fev, the fev-bench evaluation configuration of ChakraTS: no part of it was pretrained on any fev-bench dataset, so trained_on_datasets is empty and no task is flagged.

• Add the models.yaml entry (closed-api, zero-shot, commercial use permitted).

• The wrapper sends each window's items with all target columns, past covariates and known covariates, 100 items per call, and never mixes windows in one call.

Validation:

• test/test_fev_bench_submissions.py passes for the new file and metadata.

• The result CSV has 100 unique tasks and complete SQL, MASE, WAPE and WQL values.

• The wrapper run against the API reproduces the submitted rows within 0.1% on every task we checked. We will send the maintainers an evaluation key privately. -t 3 runs the three ProEnFo tasks in about 5 minutes, -t 8 about 45 minutes, the full benchmark about few hours. The server scales to zero when idle, so the first call can take a couple of minutes; the wrapper retries.

• We can also share the complete outputs of our own run privately, including the final forecasts for all 100 tasks, if that helps the review.

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