---

name: chronos-forecasting description: "Use Chronos Forecasting for pretrained time-series forecasting, Chronos-2 covariates, data validation, fine-tuning, evaluation, and deployment planning." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Chronos Forecasting repo skill

Use this repo skill when a task involves the chronos-forecasting package, Chronos-2, Chronos-Bolt, original Chronos/T5 time-series forecasting models, probabilistic forecast quantiles, pandas long-format time-series data, covariates, predict_df, predict_fev, fine-tuning, benchmark evaluation, or deployment planning.

Install and import check

Base install:

pip install chronos-forecasting

Minimal import check:

import chronos
from chronos import BaseChronosPipeline, Chronos2Pipeline, ChronosBoltPipeline, ChronosPipeline
print(chronos.__version__)

Read references/installation-and-environment.md before choosing optional extras, GPU wheels, cloud dependencies, or benchmark/training dependencies. Run scripts/chronos_api_smoke.py for a safe import/signature/backend smoke that does not download models by default.

Choose the route

User task	Read
Chronos-2 zero-shot forecasting, multivariate/covariate prediction, Chronos2Pipeline, predict, predict_quantiles, predict_df, embeddings, long horizons, or model loading	sub-skills/chronos-2-forecasting/
DataFrame schema repair, timestamp frequency, future covariate alignment, list-of-dicts validation, preprocessing helpers, or target leakage checks	sub-skills/data-formats-and-validation/
Chronos-Bolt or original Chronos/T5 models, direct quantile vs sample forecasts, univariate tensor/list inputs, or family selection among older model IDs	sub-skills/chronos-bolt-and-original/
Chronos-2 fine-tuning/LoRA, original training configs, KernelSynth, fev evaluation, aggregate relative scores, SageMaker/cloud deployment, or side-effecting benchmark/training plans	sub-skills/training-evaluation-deployment/

Shared references:

• Model family comparison and public model IDs: references/model-overview.md
• Installation, optional dependencies, CPU/GPU policy, and safe helper list: references/installation-and-environment.md
• Cross-cutting failures for imports, loading, optional extras, backend mismatch, and side-effecting workflow gates: references/troubleshooting.md
• Source snapshot and refresh baseline: references/repo-provenance.md

Operating guardrails

• Prefer BaseChronosPipeline.from_pretrained(...) when the model anchor may be any Chronos family, then inspect type(pipeline).__name__ before calling family-specific APIs.
• Do not trigger Hugging Face downloads, S3 downloads, dataset downloads, SageMaker endpoint creation, model training, or hub pushes unless the user explicitly asks and supplies the needed model/data/credential/budget context.
• Keep prediction_length, quantile_levels, batch_size, context_length, device_map, and dtype explicit in reproducible snippets.
• Validate pandas schemas before disabling validate_inputs or accepting a future_df with known-future covariates.
• Do not claim GPU, cloud, or benchmark verification from a CPU import smoke. Record those as optional/unverified unless actually executed.
• When using this skill for a different checkout, read references/repo-provenance.md; refresh if the commit, package version, public APIs, or evidence paths changed.