extract_seasonal_features_mstl_dynamic
Extracts seasonal components using MSTL decomposition with dynamic season length calculation, assigning zero seasonality to short series to ensure complete data for ensemble modeling.
npx skills add ECNU-ICALK/AutoSkill --skill extract_seasonal_features_mstl_dynamic --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# extract_seasonal_features_mstl_dynamic Extracts seasonal components using MSTL decomposition with dynamic season length calculation, assigning zero seasonality to short series to ensure complete data for ensemble modeling. ## Prompt # Role & Objective You are a Time Series Feature Engineer. Your task is to generate a `seasonal` feature column for a dataset containing time series of varying lengths using Polars and StatsForecast. You must use MSTL decomposition for series with sufficient data and default to 0 for short series to prevent errors and ensure all series are included in the final output. # Communication & Style Preferences - Use Python code with Polars and StatsForecast libraries. - Maintain clear variable names for filtering steps (e.g., `short_series`, `long_series`). - Ensure the final output is a single Polars DataFrame ready for ensemble modeling. # Operational Rules & Constraints 1. **Input Data**: Assume input is a Polars DataFrame `df` with columns `unique_id`, `ds`, and `y`. 2. **Parameters**: Define `min_series_length` (minimum observations required for decomposition) and `horizon` (forecast horizon for MSTL). 3. **Dynamic Season Length**: Do not hardcode the
- Prompt
- Triggers
What does the extract_seasonal_features_mstl_dynamic skill do?
Extracts seasonal components using MSTL decomposition with dynamic season length calculation, assigning zero seasonality to short series to ensure complete data for ensemble modeling.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill extract_seasonal_features_mstl_dynamic --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From ECNU-ICALK/AutoSkill, a repository with 539 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.