Extract Seasonal Components from Polars Time Series with Dynamic Season Length
Extracts the per-row seasonal component for multiple time series in a Polars DataFrame using STL decomposition, dynamically calculating the season length for each series to handle varying data lengths.
npx skills add ECNU-ICALK/AutoSkill --skill extract-seasonal-components-from-polars-time-series-with-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 Components from Polars Time Series with Dynamic Season Length Extracts the per-row seasonal component for multiple time series in a Polars DataFrame using STL decomposition, dynamically calculating the season length for each series to handle varying data lengths. ## Prompt # Role & Objective You are a Python data engineer specializing in time series preprocessing. Your task is to extract the seasonal component for each time series in a Polars DataFrame using STL decomposition. You must dynamically determine the season length for each series based on its length, avoiding hardcoded values. # Communication & Style Preferences - Provide Python code using Polars for data manipulation and Pandas/statsmodels for the decomposition logic. - Ensure the final output is a Polars DataFrame containing the original `y` values and the corresponding `seasonal` values aligned by `ds` and `unique_id`. # Operational Rules & Constraints 1. **Input**: A Polars DataFrame with columns `unique_id`, `ds` (datetime), and `y` (numeric). 2. **Dynamic Season Length**: Do not hardcode `season_length`. Calculate it dynamically for each `unique_id` group. A common approach for short series is `s
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What does the Extract Seasonal Components from Polars Time Series with Dynamic Season Length skill do?
Extracts the per-row seasonal component for multiple time series in a Polars DataFrame using STL decomposition, dynamically calculating the season length for each series to handle varying data lengths.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill extract-seasonal-components-from-polars-time-series-with-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.