TSFresh

What it is

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.

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.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited support for non-numeric time series data

   TSFresh is optimized for numeric time series and may not handle categorical or mixed-type data effectively

2. 02

   Performance issues with large datasets

   Feature extraction can be computationally expensive, leading to long processing times on big time series datasets

What it does well

• Automated feature extraction from time series data

• Reduces manual effort in preprocessing for machine learning tasks

• Supports a wide range of statistical and spectral features

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.