Statsforecast Polars Ensemble Pipeline

What the source does

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• DependenciesPinnedNothing floats
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Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 04e912cff54b · 14 Oct 2026Report an issue or request a re-scan

• PublisherECNU-ICALK GitHub organizationOwner of the repository this skill is read from
• Source folderECNU-ICALK/AutoSkill/SkillBank/ConvSkill/english_gpt4_8_GLM4.7/statsforecast_polars_ensemble_pipelineThe exact folder we audited
• LicenceNot declaredAs the repository states it
• Commit pinned for this verdict04e912cff54bA new audit runs when the source changes
• Repository statusNot archivedLast push 157 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: Execute a univariate time series forecasting pipeline using StatsForecast and Polars. Includes ID concatenation, cross-validation, ensemble generation (AutoARIMA, AutoETS, DynamicOptimizedTheta), non-negative…

---
id: "3540f70f-4f40-4579-b67e-d00e4a9eb9b4"
name: "statsforecast_polars_ensemble_pipeline"
description: "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."
version: "0.1.5"
tags:
  - "time-series"
  - "statsforecast"
  - "polars"
  - "ensemble-forecasting"
  - "constraints"
  - "metrics"
  - "outlier-removal"
  - "data-formatting"
  - "string-manipulation"
triggers:
  - "statsforecast polars ensemble"
  - "concatenate unique id columns"
  - "time series cross validation wmape bias"
  - "non-negative forecast constraint"
  - "split unique id polars"
  - "calculate group accuracy ignoring outliers"
  - "format forecast output with rounding and id splitting"
---

# 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`, `SalesOrg`, `DistrChan`, `CL4`, `WeekDate`, `OrderQuantity`.
- **ID Concatenation**: Concatenate `MaterialID`, `SalesOrg`, `DistrChan`, `CL4` into a new column `unique_id` using `_` as the separator. Drop the original ID columns.
- **Renaming**: Rename `WeekDate` to `ds` and `OrderQuantity` to `y`.
- **Global ID Exclusion**: Exclude specific `unique_id`s (e.g., zero-value series) at the very beginning if necessary.
- **Length-Based Filtering**: Use helper functions to group by `unique_id`, calculate counts, and filter by a `length_threshold`.
- **Temporal Sorting**: Always sort the DataFrame by the date column (`ds`) using `.sort('ds')` before any forecasting operation to prevent temporal leakage.

## 2. Model Initialization
- Import `AutoARIMA`, `AutoETS`, `DynamicOptimizedTheta` from `statsforecast.models`.
- Initialize models list with `AutoARIMA(season_length=12)`, `AutoETS(season_length=12)`, `DynamicOptimizedTheta(season_length=12)`.
- Initialize `StatsForecast` with `models=models`, `freq='1w'`, `n_jobs=-1`.

## 3. Cross-Validation
- Perform cross-validation using `sf.cross_validation(df=df, h=2, step_size=1, n_windows=8, sort_df=True)`. Ensure input is in long format (`unique_id`, `ds`, `y`).
- **Non-Negative Constraints**: Apply constraints to individual model forecast columns (e.g., 'AutoARIMA', 'AutoETS') and their prediction intervals **before** calculating the ensemble.
  - Use Polars syntax: `pl.when(pl.col('column') < 0).then(0).otherwise(pl.col('column')).alias('column')`.
- **Ensemble Calculation**: Calculate the ensemble forecast by taking the mean of the *constrained* predictions. Add the ensemble as a new column `Ensemble`.

## 4. Metrics Calculation
- **Standard Metrics**: Calculate WMAPE using `np.abs(y_true - y_pred).sum() / np.abs(y_true).sum()` and bias based on the ensemble predictions.
- **Individual Metrics**: Calculate individual accuracy: `1 - (abs(y - Ensemble) / y)`. Calculate individual bias: `(Ensemble / y) - 1`.
- **Constrained Group Metrics (Outlier Removal)**:
  - **Filtering Logic**: Filter the input DataFrame to retain only rows where the absolute value of the `individual_accuracy` column is less than or equal to a specified threshold (e.g., 15) AND the absolute value of the `individual_bias` column is less than or equal to the same threshold.
  - **Syntax**: `df.filter((pl.col('individual_accuracy').abs() <= threshold) & (pl.col('individual_bias').abs() <= threshold))`
  - **Error Recalculation**: Recalculate the errors for the filtered dataset using the formula: `errors_filtered = filtered_df['y'] - filtered_df['Ensemble']`.
  - **Group Accuracy Calculation**: Compute the group accuracy using the formula: `1 - (errors_filtered.abs().sum() / filtered_df['y'].sum())`.
  - **Group Bias Calculation**: Compute the group bias using the formula: `(filtered_df['Ensemble'].sum() / filtered_df['y'].sum()) - 1`.

## 5. Forecasting
- Fit the models on the entire dataset using `sf.fit()`.
- Instantiate `ConformalIntervals`.
- Generate forecasts using `sf.forecast(h=104, prediction_intervals=prediction_intervals, level=[95], id_col='unique_id', sort_df=True)`.

## 6. Post-Processing & Formatting
- **Forecast Constraints & Ensemble**: Apply non-negative constraints to individual model forecast columns and their intervals. Calculate the final ensemble forecast mean.
- **Ensemble Intervals**: Calculate ensemble prediction intervals (lo-95, hi-95) by averaging the individual model intervals, applying non-negative constraints first.
- **Rounding**: Round `EnsembleForecast`, `Ensemble-lo-95`, `Ensemble-hi-95` to integers using `.round().cast(pl.Int32)`.
- **ID Splitting**: Split the `unique_id` column back into original components: `MaterialID`, `SalesOrg`, `DistrChan`, `CL4`.
  - **Data Types**: Cast `MaterialID` to `pl.Int64`, `SalesOrg` to string, `DistrChan` to `pl.Int64`, a

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

git clone https://github.com/ECNU-ICALK/AutoSkill
git -C AutoSkill checkout 04e912cff54b

Works in

1. 2026-10-14
   ✓SAFE B 89 / 100

   first audit · source 04e912cff54b

Questions about Statsforecast Polars Ensemble Pipeline

• github.com/ECNU-ICALK/AutoSkill/tree/main/SkillBank/ConvSkill/en — the audited source at commit 04e912cff54b and every finding aboveObserved · 14 Oct 2026

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