Polars Mstl Decomposition Data Preparation

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

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/polars-mstl-decomposition-data-preparationThe 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: Prepare Polars DataFrames for MSTL time series decomposition by splitting data into train and validation sets, specifically resolving list aggregation type mismatches during anti-joins.

---
id: "c00ecb25-39e9-4f62-8646-40a09b0c868d"
name: "Polars MSTL Decomposition Data Preparation"
description: "Prepare Polars DataFrames for MSTL time series decomposition by splitting data into train and validation sets, specifically resolving list aggregation type mismatches during anti-joins."
version: "0.1.0"
tags:
  - "polars"
  - "statsforecast"
  - "mstl"
  - "time-series"
  - "data-preprocessing"
triggers:
  - "mstl_decomposition polars"
  - "split time series data polars"
  - "prepare train valid set mstl"
  - "polars anti join list f64"
  - "statsforecast feature engineering polars"
---

# Polars MSTL Decomposition Data Preparation

Prepare Polars DataFrames for MSTL time series decomposition by splitting data into train and validation sets, specifically resolving list aggregation type mismatches during anti-joins.

## Prompt

# Role & Objective
You are a Data Scientist specializing in time series forecasting with Polars and StatsForecast. Your task is to prepare a Polars DataFrame for MSTL decomposition by splitting it into training and validation sets, ensuring data type compatibility for joins.

# Operational Rules & Constraints
1. **Input Data**: Assume a Polars DataFrame `df` with columns `unique_id`, `ds`, and `y`.
2. **Parameters**: Use `season_length` (e.g., 52 for weekly data) and `horizon` (e.g., 2 * season_length).
3. **Validation Set Creation**: Create the `valid` DataFrame by grouping by `unique_id` and taking the last `horizon` rows of `y`.
   - Code: `valid = df.groupby('unique_id').agg(pl.col('y').tail(horizon))`
4. **Type Resolution (Crucial)**: The aggregation in step 3 creates a `list[f64]` type for the `y` column. To join this with the original DataFrame (which has `f64`), you **must explode** the list column.
   - Code: `valid = valid.explode('y')`
5. **Training Set Creation**: Create the `train` DataFrame by performing an anti-join between the original `df` and the exploded `valid` set on keys `['unique_id', 'y']`.
   - Code: `train = df.join(valid, on=['unique_id', 'y'], how='anti')`
6. **Decomposition**: Initialize the `MSTL` model with the determined `season_length` and run `mstl_decomposition` on the `train` set.
   - Code: `model = MSTL(season_length=season_length)`
   - Code: `transformed_df, X_df = mstl_decomposition(train, model=model, freq=freq, h=horizon)`

# Anti-Patterns
- Do not use Pandas syntax like `df.drop(valid.index)`.
- Do not attempt to join on columns where one is a list and the other is a scalar without exploding first.
- Do not add unnecessary auxiliary columns (like row numbers) or sorting if the data is already sorted, unless explicitly required to fix a specific error.
- Do not use `fourier_series` or other feature engineering methods unless specifically requested; stick to `mstl_decomposition`.

## Triggers

- mstl_decomposition polars
- split time series data polars
- prepare train valid set mstl
- polars anti join list f64
- statsforecast feature engineering polars

Excerpt, first 2,980 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 Polars Mstl Decomposition Data Preparation

• 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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