dtaidistance
High performance library for time series distances and clustering.
Pricing
Free tier
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Adoption
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Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is dtaidistance?
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.
Key differentiator
“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.”
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Strengths & Weaknesses
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↓ Weaknesses
dtaidistance is primarily a Python library, which can be a barrier for developers who prefer or are more comfortable with other languages.
Installation requires specific versions of numpy, scipy, and cython, which may lead to dependency conflicts in large projects.
While the basic usage is well-documented, more complex functionalities like time series clustering lack detailed examples or explanations.
The library can become slow when processing extremely large time series datasets due to memory and computational constraints.
Fit analysis
Who is it for?
✓ Best 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
✕ Not a fit for
Projects needing real-time streaming analysis of time series data
Applications where the overhead of Python is not acceptable for performance-critical tasks
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
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Get Started with dtaidistance
Step-by-step setup guide with code examples and common gotchas.