# 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.

- Skill: `ecnu-icalk/statsforecast-ensemble-pipeline-with-visualization` (Agent Skill)
- Install (CLI): `npx skillmds@latest add ecnu-icalk/statsforecast-ensemble-pipeline-with-visualization`
- Raw SKILL.md: https://api.skillmd.com/api/skills/ecnu-icalk/statsforecast-ensemble-pipeline-with-visualization/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: DevOps & Infra
- Author: ECNU-ICALK (https://skillmd.com/u/ecnu-icalk)
- Updated: 2026-09-08
- Page: https://skillmd.com/skills/ecnu-icalk/statsforecast-ensemble-pipeline-with-visualization

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# 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 suggesting colors for plots, provide specific color names (e.g., 'midnightblue', 'crimson').
- Use clear, concise explanations for code logic.

# Operational Rules & Constraints
1. **Data Preprocessing**:
   - If the input data is not in StatsForecast format, perform the following:
     - Filter the dataset for specific items if required.
     - Convert the date column (e.g., 'WeekDate') to datetime format.
     - Group by keys (e.g., 'MaterialID', 'SalesOrg', 'DistrChan', 'CL4') and the date column, aggregating quantities (e.g., sum).
     - Sort the data by the date column.
     - Create a 'unique_id' column by concatenating the key columns with an underscore separator.
     - Rename the date column to 'ds' and the target column to 'y'.
   - Filter out time series with fewer than a specified minimum length (e.g., 16 weeks) to ensure model stability.

2. **Model Initialization**:
   - Import `StatsForecast`, `AutoARIMA`, `AutoETS`, `DynamicOptimizedTheta`, `ConformalIntervals` from `statsforecast`. Import `polars` as `pl`, `numpy` as `np`, and `matplotlib.pyplot` as `plt`.
   - Set Polars display config: `pl.Config.set_tbl_rows(None)`.
   - Initialize models with **fixed season_length of 52**: `AutoARIMA(season_length=52)`, `AutoETS(damped=True, season_length=52)`, `DynamicOptimizedTheta(season_length=52)`.
   - Initialize `StatsForecast` with `models`, `freq='1w'`, and `n_jobs=-1`.

3. **Cross-Validation**:
   - Perform cross-validation using `sf.cross_validation(df=..., h=5, step_size=1, n_windows=10, sort_df=True)`.

4. **Ensemble Aggregation**:
   - Calculate the ensemble value (Mean) across the prediction columns (`AutoARIMA`, `AutoETS`, `DynamicOptimizedTheta`).
   - **Loop-Safe Polars Syntax**: To prevent 'duplicate column name' errors during iterative processes (e.g., cross-validation), strictly follow this 3-step workflow:
     1. Calculate the row-wise aggregation. Do **not** use `.alias()` in this step.
     2. Create a `pl.Series` from the calculated values.
     3. Add the Series to the DataFrame using `with_columns` or `hstack`.
   - Do not use `axis=1` (Pandas syntax).

5. **Metrics Calculation**:
   - Define WMAPE as: `np.abs(y_true - y_pred).sum() / np.abs(y_true).sum()`.
   - Calculate individual accuracy: `1 - (abs(y_true - y_pred) / y_true)`.
   - Calculate individual bias: `(y_pred / y_true) - 1`.
   - Calculate group accuracy and group bias over all folds.

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

7. **Post-Processing**:
   - **Non-negative Constraint**: Apply `pl.when(pl.col(col) < 0).then(0).otherwise(pl.col(col))` to **all** forecast columns and their prediction intervals (lo-95, hi-95).
   - **Ensemble Forecast**: Calculate the ensemble forecast using the mean of individual models, adhering to the loop-safe 3-step structure.
   - **Ensemble Intervals**: Calculate the ensemble prediction intervals (lo-95, hi-95) as the mean of the individual model intervals.
   - **ID Splitting**: Split the 'unique_id' column back into original columns (`MaterialID`, `SalesOrg`, `DistrChan`, `CL4`). Use Polars native methods (e.g., `str.split('_').list.to_struct(...)`) to avoid Pandas syntax. Pad with `None` if fewer than 4 parts exist.
   - **Renaming**: Rename `ds` to `WeekDate`.
   - **Reordering**: Select columns in the specific order: `MaterialID`, `SalesOrg`, `DistrChan`, `CL4`, `WeekDate`, `EnsembleForecast`, `Ensemble-lo-95`, `Ensemble-hi-95`, followed by individual model columns and their intervals.
   - **Rounding**: Round ensemble forecasts and intervals to integers using `.round().cast(pl.Int32)`.

8. **Visualization**:
   - Loop through unique IDs to plot historical vs forecasted data with prediction intervals.
   - Use contrasting colors for visibility (e.g., dark historical line, distinct forecast line, light gray interval).

# Anti-Patterns
- Do not change the season_length from 52 unless explicitly requested.
- Do not omit the non-negative constraint step for any forecast column or interval.
- Do not use `.alias()` immediately after the calculation expression for ensemble columns inside loops (e.g., cross-validation), as this causes duplicate column errors.
- Do not combine calculation and column addition into a single chained expression if it risks the duplicate column error.
- Do not use `df[['col1', 'col2']].mean(axis=1)` as this is Pandas syntax and fails in Polars.
- Do not use `sort_values` for Polars DataFrames; use `sort`.
- Do not use Pandas-specific string splitting syntax like `str.split('_', expand=True)`; use Polars-native methods like `str.split_by`.
- Do not invent model explanations; stick to the user's provided definitions or standard documentation.
- Do not modify the user's specific variable names (e.g., `y_cl4`, `forecasts_df`) unless generalizing the concept.

# Interaction Workflow
1. Receive the input DataFrame (raw or pre-filtered).
2. Execute the pipeline steps sequentially (Preprocess -> Filter -> Fit -> Forecast -> Process).
3. Output the final formatted DataFrame (`forecasts_df`) and print WMAPE/Accuracy metrics.
4. Generate visualization plots for the forecasted series.

## Triggers

- ensemble model statsforecast polars
- time series forecasting pipeline wmape
- visualize ensemble forecasts with prediction intervals
- run the statsforecast pipeline
- format forecast output with unique_id split