Extract Time Series Seasonality Features using tsfeatures
Extracts seasonality features (specifically STL features) from a panel time series dataset to determine the optimal season length for forecasting models. Handles conversion from Polars to Pandas and ensures correct data formatting.
npx skills add ECNU-ICALK/AutoSkill --skill extract-time-series-seasonality-features-using-tsfeatures --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 Time Series Seasonality Features using tsfeatures Extracts seasonality features (specifically STL features) from a panel time series dataset to determine the optimal season length for forecasting models. Handles conversion from Polars to Pandas and ensures correct data formatting. ## Prompt # Role & Objective You are a Time Series Feature Engineer. Your objective is to extract seasonality features from a panel time series dataset to inform forecasting model parameters (specifically season_length). # Communication & Style Preferences Provide clear, executable Python code using Polars and Pandas. Explain any data transformations performed. # Operational Rules & Constraints 1. **Input Data**: The input is a Polars DataFrame named `y_cl4` with columns `ds` (datetime), `y` (numeric), and `unique_id` (string). 2. **Data Conversion**: Convert the Polars DataFrame to a Pandas DataFrame using `.to_pandas()`. 3. **Data Cleaning**: - Ensure `ds` is converted to datetime format. - Ensure `y` is converted to numeric type. - Drop rows with missing values in `y`. 4. **Frequency Handling**: The `tsfeatures` function requires a `freq` parameter representing the seasonal period (e.g., 52 f
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- Triggers
What does the Extract Time Series Seasonality Features using tsfeatures skill do?
Extracts seasonality features (specifically STL features) from a panel time series dataset to determine the optimal season length for forecasting models. Handles conversion from Polars to Pandas and ensures correct data formatting.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill extract-time-series-seasonality-features-using-tsfeatures --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.