---

name: data-formats-and-validation description: "Validate and prepare Chronos long-format data, covariates, timestamps, and preprocessing inputs before forecasting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Data Formats and Validation

Use this sub-skill when the task is about preparing Chronos inputs, checking DataFrame schemas, or debugging preprocessing before forecasting.

Covers

• Long-format pandas DataFrames for Chronos2Pipeline.predict_df
• chronos.df_utils normalization, validation, and future-timestamp generation
• chronos.chronos2.preprocess helpers for tensors, list-of-tensors, DataFrames, and list-of-dicts
• Target and covariate alignment, timestamp frequency rules, categorical covariates, and validate_inputs=False hazards

Route elsewhere

• Model loading, forecasting, embeddings, or output interpretation: ../chronos-2-forecasting/ or ../chronos-bolt-and-original/
• Fine-tuning, evaluation, benchmark configs, or deployment: ../training-evaluation-deployment/

Read first

1. references/data-formats.md
2. references/preprocessing-api-reference.md
3. references/troubleshooting.md
4. scripts/validate_chronos_dataframe.py --help

Runtime files

• references/data-formats.md
• references/preprocessing-api-reference.md
• references/troubleshooting.md
• scripts/validate_chronos_dataframe.py

Use this sub-skill to answer

• Which columns Chronos expects in df and future_df
• How future_df is matched to context rows
• Which helper to use for DataFrame, list-of-dicts, or tensor inputs
• How Chronos handles categorical covariates and known-future values
• What breaks when validation is skipped