dtaidistance
dtaidistance calculates time-series distances using a fast dynamic time warping implementation.
About dtaidistance
dtaidistance is a Python library for measuring distances between time series, mainly using Dynamic Time Warping (DTW). It provides both a pure Python implementation and a faster implementation in C. It is developed by the DTAI Research Group and is installable with pip or conda-forge.
The library accepts Numpy arrays and Pandas data, and tuning arguments such as window, max_dist, and max_step let users limit or stop computations early. Recent versions add subsequence search, support for multivariate series, and DTW Barycenter Averaging for clustering. The fastest C path requires array input with the double type. Parallel execution in C is included only when OpenMP is installed.
Key features
- Dynamic Time Warping distance between two numeric sequences
- Pure Python and C-based implementations of DTW
- Warping path computation with optional plot output
- Subsequence search and local concurrence detection
- Support for multivariate time series
- DTW Barycenter Averaging for clustering
Best for: Python developers who need to compare or cluster time series using Dynamic Time Warping.
Good for
- The C implementation has Cython as its only dependency.
- It is compatible with Numpy and Pandas and avoids unnecessary data copy operations.
- Options such as window and max_dist can stop some computations early.
Watch out for
- The fastest C path requires array input with the double type.
- Parallel execution in C is included only if OpenMP is installed.
- Compilation problems may require the extra options described in the documentation.
dtaidistance at a glance
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 6, 2026 · Online when checked
- Listed
- Oct 5, 2026
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dtaidistance FAQ
How can I install dtaidistance?
It can be installed with pip install dtaidistance or conda install -c conda-forge dtaidistance.
Does dtaidistance work with Numpy and Pandas?
The library is compatible with Numpy and Pandas. Numpy is an optional dependency, but it is needed to compile the C-based code from the pip installation.
What arguments can be used to tune the distance calculation?
The documented options include window, max_dist, max_step, max_length_diff, penalty, and psi. Each one limits or adjusts the warping computation.
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