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sktime-tsfresh

Transform time series data into tabular features with sktime and tsfresh.

19|2|Updated Jan 31, 2026
Search Tags:#machine learning#time series#feature extraction#data preprocessing#sktime#tsfresh
One-click install
npx skills add https://github.com/tondevrel/scientific-agent-skills --skill sktime-tsfresh
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sktime-tsfresh
Source: https://github.com/tondevrel/scientific-agent-skills/tree/main/skills/sktime%20%26%20tsfresh
Command: npx skills add https://github.com/tondevrel/scientific-agent-skills --skill sktime-tsfresh

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill transforms raw time series data into tabular features, making it suitable for classical machine learning models, and automates feature extraction and selection to prevent data leakage.

Core Features & Use Cases

  • Automated Feature Extraction: Leverages tsfresh to extract a wide array of statistical and spectral features from time series.
  • Pipeline Integration: Seamlessly integrates tsfresh into sktime pipelines, ensuring feature extraction happens only on training data during cross-validation.
  • Feature Relevance Selection: Uses the FRESH algorithm to filter out non-informative features, reducing dimensionality and improving model interpretability.
  • Use Case: When building a time series classification model, use this skill to convert multiple sensor readings (time series) into a set of relevant features for a RandomForestClassifier.

Quick Start

Use the sktime-tsfresh skill to create a pipeline that extracts efficient tsfresh features and trains a RandomForestClassifier on the provided time series data X and labels y.

Frequently Asked Questions about sktime-tsfresh

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

FAQPage Schema
How do I extract tabular features from time series data for machine learning?▼

You extract tabular features from time series data by transforming raw time-stamped observations into statistical and spectral features. This process prepares panel data or sensor readings for classical classification and regression models.

How do I prevent data leakage when extracting features from time series data?▼

To prevent data leakage during feature extraction, you must ensure features are generated only from training data folds during cross-validation. Integrating feature extraction directly into a pipeline restricts extraction to the training set automatically.

What is the best way to automate feature selection for time series classification?▼

The best way to automate feature selection for time series classification is using the FRESH algorithm to filter out non-informative features. This reduces dimensionality and improves model interpretability before training your classifier.

Can I use tsfresh feature extraction inside an sktime pipeline?▼

Yes, you can use tsfresh feature extraction inside an sktime pipeline to seamlessly integrate automated feature generation. This ensures feature extraction happens only on training data during cross-validation for your machine learning models.

Does converting time series to tabular features work for sensor readings?▼

Yes, converting time series to tabular features works perfectly for sensor readings and other time-stamped observations. It extracts a wide array of relevant features suitable for training models like a RandomForestClassifier.

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