Sequentia is a collection of machine learning algorithms for performing the classification of isolated temporal sequences.
Each isolated sequence is generally modeled as a section of a longer multivariate time series that represents the entire sequence. Naturally, this fits the description of many types of problems such as:
- isolated word utterance frequencies in speech audio signals,
- isolated hand-written character pen-tip trajectories,
- isolated hand or head gestures positions in a video or motion-capture recording.
Most modern machine learning algorithms won’t work directly out of the box when applied to such sequential data – mostly due to the fact that the dependencies between observations at different time frames must be considered, and also because each isolated sequence generally has a different duration.
Sequentia offers some appropriate classification algorithms for these kinds of tasks.