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time-series-features

Generate classical time-series features like lags and rolling windows.

1|Updated Jun 4, 2026
Search Tags:#machine learning#feature engineering#forecasting#time-series#sales data
One-click install
npx skills add https://github.com/hung-phan/ml-skills --skill time-series-features
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: time-series-features
Source: https://github.com/hung-phan/ml-skills/tree/main/skills/ml-review/references/data-prep/time-series-features
Command: npx skills add https://github.com/hung-phan/ml-skills --skill time-series-features

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill provides tools and guidance for crafting high-quality features from time-series data, crucial for forecasting, machine learning models, and data science analysis.

Core Features & Use Cases

  • Feature Engineering: Offers a variety of classical feature engineering techniques tailored for time-series data, such as lags, rolling windows, and point-process features.
  • Avoiding Leakage: Helps to create time-series datasets without future leakage, suitable for various predictive modeling tasks like demand forecasting and sales prediction.
  • Use Case: With this Skill, a data scientist can generate features from retail sales data to feed into a machine learning model for demand prediction.

Quick Start

Analyze the sales data of the past week by running the script analyze_sales.py.

Frequently Asked Questions about time-series-features

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I create time-series features for machine learning forecasting without data leakage?▼

Time-series features can be generated without future leakage using classical techniques like lags and rolling windows, ensuring temporal coherence for machine learning models. This approach provides data integrity for predictive tasks.

What classical techniques are used for feature engineering on sales data?▼

Classical feature engineering for sales data utilizes lags, rolling windows, and point-process features to extract temporal patterns. These methods transform raw temporal data into structured inputs for demand forecasting.

Can I use this time-series feature engineering for traffic prediction tasks?▼

Yes, time-series feature engineering supports traffic prediction by generating temporally coherent datasets. It creates structured lag and rolling window features suitable for training predictive analytics models.

How do I analyze retail sales data to generate inputs for demand prediction models?▼

You can analyze retail sales data by running the analyze_sales script to generate classical time-series features. This process creates lag and rolling window inputs needed for demand prediction machine learning models.

What is the best way to ensure temporal coherence when building forecasting datasets?▼

Ensuring temporal coherence in forecasting datasets involves applying classical feature engineering techniques that respect chronological order. This prevents future data leakage and maintains data integrity for machine learning analysis.

Do I need any specific dependencies to generate time-series features for my models?▼

No external dependencies are required to generate time-series features. The Skill provides self-contained scripts and references to create lag and rolling window features for your forecasting models.

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