Data ScientistFeature EngineeringIntermediateSingle prompt

Lag and Rolling Features AI Prompt

This prompt builds historical lag and rolling statistics for panel or time series data without leaking future information. It is meant for forecasting, churn, risk, and behavioral models where recent history is often the strongest signal. The structure keeps everything grouped by entity and aligned to prediction time.

Prompt text
Create lag and rolling window features for this time-ordered dataset.

Assume the data is ordered by {{date_column}} with one row per {{entity_column}} per time period.

Create per entity:
- Lag features: value at t-1, t-2, t-3, t-7, t-14, t-28 periods back
- Rolling mean: 7-period, 14-period, 28-period window
- Rolling standard deviation: 7-period and 28-period window
- Rolling min and max: 7-period window
- Exponentially weighted moving average (alpha=0.3)
- Trend: slope of a linear regression fitted on the last 7 values

Critical: ensure no data leakage — all features must use only information available at prediction time (strictly historical).

Return the feature creation code and confirm the leakage-free construction.

When to use this prompt

Use case 01

Your rows are ordered by time and tied to entities such as users, stores, or products.

Use case 02

You want classic time-aware features like lags, rolling averages, and trends.

Use case 03

Leakage prevention is critical because future values must never influence past predictions.

Use case 04

You need reusable pandas code for a forecasting or panel-data pipeline.

What the AI should return

Leakage-free feature engineering code for lags, rolling windows, EWMA, and trend features, along with a short explanation confirming that all features use strictly historical information only.

How to use this prompt

1

Open your data context

Load your dataset, notebook, or working environment so the AI can operate on the actual project context.

2

Copy the prompt text

Use the copy button above and paste the prompt into the AI assistant or prompt input area.

3

Review the output critically

Check whether the result matches your data, assumptions, and desired format before moving on.

4

Chain into the next prompt

Once you have the first result, continue deeper with related prompts in Feature Engineering.

Frequently asked questions

What does the Lag and Rolling Features prompt do?+

It gives you a structured feature engineering starting point for data scientist work and helps you move faster without starting from a blank page.

Who is this prompt for?+

It is designed for data scientist workflows and marked as intermediate, so it works well as a guided starting point for that level of experience.

What type of prompt is this?+

Lag and Rolling Features is a single prompt. You can copy it as-is, adapt it, or use it as one step inside a larger workflow.

Can I use this outside MLJAR Studio?+

Yes. The prompt text works in other AI tools too, but MLJAR Studio is the best fit when you want local execution, visible Python code, and reusable notebooks.

What should I open next?+

Natural next steps from here are Date Feature Extraction, Embedding Features from Text, Feature Ideas Generator.