Add time-series-forecasting task schema

[pjhul]

Scaffolds the new time-series-forecasting task with input/output JSON schemas, generated TypeScript types, task metadata, and registry wiring. pnpm check, test, and lint:check on the tasks package all pass locally.

Design decisions worth flagging

1. inputs is a list of structured series objects, not a string or binary payload. This diverges from existing HF tasks but aligns closely with mature time-series libraries (Darts, GluonTS, AWS SageMaker Chronos). Parallel-array encodings don't survive contact with ragged lengths or per-series optional fields (timestamps, covariates, metadata).

2. target is always 2D [num_timesteps][num_channels]. Univariate series use a length-1 channel axis. Avoids oneOf 1D/2D ambiguity at length-1 inputs.

3. Missing observations are null inline inside target, not a separate observed_mask. Matches pandas, R, SQL, Arrow convention. HF's TimeSeriesTransformer uses past_observed_mask because PyTorch tensors can't hold null — but for API consumers dealing with a separate mask is much more cumbersome than in-line null's.

4. Input uses start + parameters.frequency rather than an explicit timestamps array. Matches the Darts TimeSeries model.

5. quantile_predictions is an array of {level, values} objects rather than a dict keyed by string-float. Matches HF's pattern for enumerated scored outputs (text-generation logprobs, classification scores, fill-mask tokens). AWS Chronos uses a dict-keyed-by-string; we diverge intentionally for HF-ecosystem consistency.

6. Three uncertainty channels on output — mean (required), quantile_predictions, samples. All optional; they may coexist. No parametric distribution parameters. samples is the universal fallback for any distributional output.

Validation

• pnpm --filter tasks-gen inference-codegen regenerates clean inference.ts from the JSON schemas
• pnpm --filter @huggingface/tasks check (tsc)
• pnpm --filter @huggingface/tasks test
• pnpm --filter @huggingface/tasks lint:check
• Registered in packages/tasks/src/tasks/index.ts

---

Note

Low Risk
Additive task scaffolding and type exports only; no runtime inference or auth changes in this diff.

Overview
This PR fully wires the time-series-forecasting pipeline in @huggingface/tasks, replacing the previous undefined task entry with schemas, generated types, Hub task content, and registry exports.

Inference contract: New JSON Schema input/output specs define batch requests as a series array of structured objects. Each series uses a required 2D target [timesteps][channels] (univariate via length-1 channels), optional start + parameters.frequency for timestamps, covariates (past_ / future_ / static), and echoed metadata. Parameters cover prediction_length, quantile_levels, num_samples, and seed. Responses return per-series outputs with required mean, optional quantile_predictions as {level, values} arrays, optional samples, and optional forecast timestamps.

Package integration: inference.ts is codegen from those schemas; public types are re-exported from tasks/index.ts. Task page data (data.ts), docs (about.md), demo/metrics/models, and TASKS_DATA["time-series-forecasting"] now use the real module instead of a placeholder.

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[hanouticelina]

Hi @pjhul, did a first pass to the PR, thanks a lot for working at this!

could you share links to the schemas/docs you used as reference?
When I asked an AI agent to cross-check, some of the claims didn't quite hold against current docs:

• Darts is 3D, not 2D. docs say "Return a 3-D array of dimension (time,
  component, sample)"
• GluonTS is 1D for univariate and 2D (dim, num_time_steps) for multivariate
• Chronos-1 is 1D-only, predict accepts "a 1D tensor, or a list of 1D
  tensors, or a 2D tensor whose first dimension is batch"* Chronos-2 adds
  multivariate, so if you used Chronos-2 the prose just needs narrowing.
• DeepAR is 1D with "NaN" strings: "target": [4.0, 10.0, "NaN", 100.0, 113.0]

want to make sure it's comparing against the same references you used.

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Cursor Bugbot has reviewed your changes using default effort and found 1 potential issue.

There are 2 total unresolved issues (including 1 from previous review).

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