TSFresh
Automated feature extraction from time series data for machine learning.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is TSFresh?
TSFresh is a Python library that automatically extracts meaningful features from time series data, simplifying the preprocessing step in machine learning workflows and enabling more accurate models.
Key differentiator
“TSFresh stands out by offering a comprehensive set of automated feature extraction methods specifically tailored for time series data, reducing the need for manual feature engineering and accelerating model development.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
API requires Python-specific patterns, TypeScript SDK is community-maintained
v0.1 to v0.2 migration required rewriting chain definitions
TSFresh is optimized for numeric time series and may not handle categorical or mixed-type data effectively
Feature extraction can be computationally expensive, leading to long processing times on big time series datasets
Fit analysis
Who is it for?
✓ Best for
Data scientists who need to quickly extract features from large datasets without manual intervention.
Machine learning teams working with complex time-series data that require extensive preprocessing.
✕ Not a fit for
Projects requiring real-time feature extraction, as TSFresh is designed for batch processing.
Applications where the overhead of Python execution significantly impacts performance.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
Alternatives
Works well with
Next step
Get Started with TSFresh
Step-by-step setup guide with code examples and common gotchas.