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dtaidistance

High performance library for time series distances and clustering.

Status
Generally available
Last checked
Checked · Oct 8, 2026
License
Open source
Pricing
Free tier · from $0
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What it is

dtaidistance is a Python library that provides high-performance implementations of Dynamic Time Warping (DTW) and other distance measures for time series analysis, along with tools for time series clustering. It's particularly useful for researchers and developers working on time series data.

dtaidistance stands out with its high-performance DTW implementation and efficient clustering capabilities, making it a go-to library for researchers and developers working on complex time series data.

When it fits — and when it doesn’t

A good fit for

  • Researchers who need high-performance DTW for large datasets

  • Data scientists working with complex time series data requiring precise clustering

  • Developers building applications that require efficient distance measures between time series

Look elsewhere if

  • Projects needing real-time streaming analysis of time series data

  • Applications where the overhead of Python is not acceptable for performance-critical tasks

Known weaknesses

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

  1. 01

    Limited language support

    dtaidistance is primarily a Python library, which can be a barrier for developers who prefer or are more comfortable with other languages.

  2. 02

    Complex setup and dependencies

    Installation requires specific versions of numpy, scipy, and cython, which may lead to dependency conflicts in large projects.

  3. 03

    Poor documentation for advanced features

    While the basic usage is well-documented, more complex functionalities like time series clustering lack detailed examples or explanations.

  4. 04

    Performance issues with very large datasets

    The library can become slow when processing extremely large time series datasets due to memory and computational constraints.

What it does well

  • High-performance implementation of Dynamic Time Warping (DTW)

  • Support for time series clustering

  • Efficient distance measures for time series analysis

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.

Performance benchmarks

How Fast Is It?

Set up dtaidistance

Setup steps, common gotchas and what to check after 30 days.

Open the setup guide →