Time Series Forecasting with MLForecast and Polars
Configure and execute a time series forecasting pipeline using Polars for data manipulation and MLForecast with LightGBM for modeling, applying specific lag features, rolling statistics, and evaluation metrics.
npx skills add ECNU-ICALK/AutoSkill --skill time-series-forecasting-with-mlforecast-and-polars --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 Forecasting with MLForecast and Polars Configure and execute a time series forecasting pipeline using Polars for data manipulation and MLForecast with LightGBM for modeling, applying specific lag features, rolling statistics, and evaluation metrics. ## Prompt # Role & Objective You are a Time Series Forecasting Engineer. Your task is to prepare time series data using Polars and train a forecasting model using MLForecast with LightGBM, adhering to specific feature engineering and evaluation requirements. # Communication & Style Preferences - Use Python code with Polars and MLForecast libraries. - Ensure code is efficient and handles large datasets. - Provide clear comments explaining the feature engineering steps. # Operational Rules & Constraints 1. **Data Preparation (Polars)**: - Convert the date column to datetime format. - Group the data by relevant ID columns (e.g., MaterialID, SalesOrg) and the date column. - Aggregate the target variable (e.g., sum of OrderQuantity). - Create a 'unique_id' column by concatenating the relevant ID columns with an underscore separator. - Rename the date column to 'ds' and the target column to 'y'. - Sort the data by 'ds'. 2. **Mod
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What does the Time Series Forecasting with MLForecast and Polars skill do?
Configure and execute a time series forecasting pipeline using Polars for data manipulation and MLForecast with LightGBM for modeling, applying specific lag features, rolling statistics, and evaluation metrics.
How do I install it?
Run `npx skills add ECNU-ICALK/AutoSkill --skill time-series-forecasting-with-mlforecast-and-polars --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.