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A machine learning interface for isolated temporal sequence classification algorithms in Python.

Project description


A machine learning interface for isolated temporal sequence classification algorithms in Python.


Temporal sequences are sequences of observations that occur over time. Changing patterns over time naturally provide many interesting opportunities and challenges for machine learning.

This library specifically aims to tackle classification problems for isolated temporal sequences by creating an interface to a number of classification algorithms.

Despite these types of sequences sounding very specific, you probably observe some of them on a regular basis!

Some examples of classification problems for isolated temporal sequences include classifying:

  • isolated word utterances in speech audio signals,
  • isolated hand-written characters according to their pen-tip trajectories,
  • isolated hand or head gestures in a video or motion-capture recording.


Sequentia offers the use of multivariate observation sequences with differing durations in conjunction with the following algorithms and methods.

Classication algorithms

  • [x] Hidden Markov Models (via Pomegranate [1])
    • [x] Multivariate Gaussian Emissions
    • [ ] Gaussian Mixture Model Emissions (soon!)
    • [x] Left-Right and Ergodic Topologies
  • [x] Approximate Dynamic Time Warping k-Nearest Neighbors (implemented with FastDTW [2])
  • [ ] Long Short-Term Memory Networks (soon!)

Preprocessing methods

  • [x] Centering and standardization
  • [x] Downsampling (decimation and averaging)
  • [x] Filtering (mean and median)
  • [x] Discrete Fourier Transform


  • [x] Multi-processing for DTW k-NN predictions

Disclaimer: The package currently remains largely untested and is still in its early pre-alpha stages – use with caution!


pip install sequentia


Documentation for the package is available on Read The Docs.

Tutorials and examples

For tutorials and examples on the usage of Sequentia, look at the notebooks here!


[1] Jacob Schreiber. "pomegranate: Fast and Flexible Probabilistic Modeling in Python." Journal of Machine Learning Research 18 (2018), (164):1-6.
[2] Stan Salvador, and Philip Chan. "FastDTW: Toward accurate dynamic time warping in linear time and space." Intelligent Data Analysis 11.5 (2007), 561-580.


All contributions to this repository are greatly appreciated. Contribution guidelines can be found here.

Edwin Onuonga
Edwin Onuonga

✉️ 🌍

Sequentia © 2019-2020, Edwin Onuonga - Released under the MIT License.
Authored and maintained by Edwin Onuonga.

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