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name: time-series-and-data description: "Use Darts TimeSeries constructors, shapes, static covariates, grouping, slicing, and export workflows safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Time series and data

Use this sub-skill when the user needs to create, inspect, reshape, split, combine, or export Darts TimeSeries objects before modeling.

Read first

• references/data-formats.md for pandas/Series/array/grouped constructors, missing dates, shape semantics, static covariates, and export patterns.
• references/api-reference.md for important constructor signatures and validation checks.
• references/troubleshooting.md for date-frequency, static covariate, component/sample, and grouped-data errors.
• scripts/timeseries_doctor.py for a tiny self-contained construction and validation smoke.

Route by user task

• DataFrame or Series to Darts: use TimeSeries.from_dataframe(), from_series(), or from_times_and_values() patterns in data-formats.md.
• Missing or irregular timestamps: validate duplicates/frequency first; use fill_missing_dates=True plus explicit freq when Darts should insert missing time points.
• Multiple value columns: explain that they become components in one multivariate TimeSeries, not separate series unless grouped intentionally.
• Many entities/stores/items: use TimeSeries.from_group_dataframe() to return a list of series, preserving static/metadata columns where appropriate.
• Static covariates: validate one global row or one row per component; keep component names aligned.
• Modeling next: route scaling/covariates to ../data-processing-and-covariates/, forecasting to ../forecasting-workflows/ or ../torch-and-foundation-models/, and metrics to ../evaluation-and-explainability/.

Safe check

From this sub-skill directory or by passing the full script path:

python scripts/timeseries_doctor.py --json

The script creates generated in-memory data, including a missing date and static covariates. It does not read source repo datasets or notebooks.

Boundaries

This sub-skill owns Darts data representation, not preprocessing pipelines, model selection, anomaly scoring, or metric interpretation. Do not instruct future agents to open original repository docs/notebooks for data patterns; the required constructor and validation details are distilled here.