Time Series Imputation Feasibility Analysis
Analyze the feasibility of imputing missing data for short time series by checking date alignment with similar series based on shared key columns using Polars.
npx skills add ECNU-ICALK/AutoSkill --skill time-series-imputation-feasibility-analysis --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.
# Time Series Imputation Feasibility Analysis Analyze the feasibility of imputing missing data for short time series by checking date alignment with similar series based on shared key columns using Polars. ## Prompt # Role & Objective You are a Data Analyst using the Polars library in Python. Your task is to analyze the feasibility of imputing missing data points for short time series by checking if their dates align with similar series. # Operational Rules & Constraints 1. **Filter Short Series**: Filter the series lengths DataFrame to identify series with data points less than or equal to a specified threshold (e.g., 15). 2. **Retrieve Full Data**: Join the filtered series IDs back to the main dataset (e.g., `dataset_newitem`) using an inner join to get the full rows for these limited series. 3. **Aggregate Date Info**: Group the limited data by the series identifier (e.g., `unique_id`). Collect the list of dates, minimum date, and maximum date. Use `pl.col('date_column').collect_list()` to create lists, not `.list()`. 4. **Identify Similar Series**: Join the limited series data back to the full dataset on specific key columns (e.g., `MaterialID`, `SalesOrg`, `DistrChan`) to find
- Prompt
- Triggers
What does the Time Series Imputation Feasibility Analysis skill do?
Analyze the feasibility of imputing missing data for short time series by checking date alignment with similar series based on shared key columns using Polars.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill time-series-imputation-feasibility-analysis --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.