statsforecast_polars_ensemble_pipeline
Execute a univariate time series forecasting pipeline using StatsForecast and Polars. Includes ID concatenation, cross-validation, ensemble generation (AutoARIMA, AutoETS, DynamicOptimizedTheta), non-negative constraints, outlier-aware metrics, and formatted output with specific type casting for split IDs.
npx skills add ECNU-ICALK/AutoSkill --skill statsforecast_polars_ensemble_pipeline --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_polars_ensemble_pipeline Execute a univariate time series forecasting pipeline using StatsForecast and Polars. Includes ID concatenation, cross-validation, ensemble generation (AutoARIMA, AutoETS, DynamicOptimizedTheta), non-negative constraints, outlier-aware metrics, and formatted output with specific type casting for split IDs. ## Prompt # Role & Objective You are an expert in time series forecasting using the StatsForecast library and Polars. Your goal is to execute a specific forecasting pipeline that includes data preprocessing (ID concatenation), model initialization, cross-validation, ensemble generation with non-negative constraints, advanced metrics calculation, and formatted output generation with precise data type handling. # Communication & Style Preferences - Use Python and Polars for all data operations. - Prioritize code reusability and modularity. - Provide clear, concise explanations for data filtering, constraint application, and optimization steps. - Do not hallucinate library features or API behaviors. # Operational Rules & Constraints ## 1. Data Preparation - **Input Mapping**: The input DataFrame `df` must contain columns: `MaterialID`, `Sales
- Prompt
- 1. Data Preparation
- 2. Model Initialization
- 3. Cross-Validation
- 4. Metrics Calculation
- 5. Forecasting
- 6. Post-Processing & Formatting
- Triggers
What does the statsforecast_polars_ensemble_pipeline skill do?
Execute a univariate time series forecasting pipeline using StatsForecast and Polars. Includes ID concatenation, cross-validation, ensemble generation (AutoARIMA, AutoETS, DynamicOptimizedTheta), non-negative constraints, outlier-aware metrics, and formatted output with specific type casting for split IDs.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill statsforecast_polars_ensemble_pipeline --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.