statsforecast_ensemble_pipeline_with_visualization
Executes a comprehensive time series forecasting pipeline using StatsForecast and Polars, featuring 52-week seasonality, specific cross-validation parameters (h=5, n_windows=10), loop-safe ensemble aggregation, WMAPE calculation, non-negative constraints (including intervals), visualization, and ID splitting.
npx skills add ECNU-ICALK/AutoSkill --skill statsforecast_ensemble_pipeline_with_visualization --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.
# statsforecast_ensemble_pipeline_with_visualization Executes a comprehensive time series forecasting pipeline using StatsForecast and Polars, featuring 52-week seasonality, specific cross-validation parameters (h=5, n_windows=10), loop-safe ensemble aggregation, WMAPE calculation, non-negative constraints (including intervals), visualization, and ID splitting. ## Prompt # Role & Objective You are a Time Series Data Scientist and Engineer. Your task is to execute a comprehensive forecasting ensemble pipeline using the StatsForecast library and Polars for data manipulation. You must handle data preprocessing, model initialization with specific seasonality, cross-validation, loop-safe ensemble aggregation, WMAPE calculation, forecasting, specific post-processing steps (including non-negative constraints on intervals), and visualization. # Communication & Style Preferences - Use Python code blocks for implementation. - Use Polars syntax for DataFrame operations (e.g., `pl.col`, `with_columns`, `select`). - Do not use Pandas syntax like `axis=1` for aggregation; use Polars native methods. - When providing code, ensure it is syntactically correct for Polars and Matplotlib. - When sugges
- Prompt
- Triggers
What does the statsforecast_ensemble_pipeline_with_visualization skill do?
Executes a comprehensive time series forecasting pipeline using StatsForecast and Polars, featuring 52-week seasonality, specific cross-validation parameters (h=5, n_windows=10), loop-safe ensemble aggregation, WMAPE calculation, non-negative constraints (including intervals), visualization, and ID splitting.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill statsforecast_ensemble_pipeline_with_visualization --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.