About

Sequentia is a Python package that provides various classification algorithms for sequential data, including classifiers based on hidden Markov models and dynamic time warping.

Some examples of how Sequentia can be used in sequence classification include:

• determining a spoken word based on its audio signal or alternative representations such as MFCCs,

• identifying heart conditions such as arrhythmia from ECG signals,

• predicting motion intent for gesture control from sEMG signals,

• classifying hand-written characters according to their pen-tip trajectories.

Classifiers and Models

• Dynamic Time Warping k-Nearest Neighbors Classifier (KNNClassifier)
• Hidden Markov Model with Gaussian Mixture Model emissions (GMMHMM)
• Hidden Markov Model Classifier (HMMClassifier)

Preprocessing Methods

• Introduction to Preprocessing
• Custom Transformations (Custom)
• Constant Trimming (TrimConstants)
• Min-max Scaling (MinMaxScale)
• Centering (Center)
• Standardizing (Standardize)
• Downsampling (Downsample)
• Filtering (Filter)
• Combined Preprocessing (Compose)

Datasets

• Introduction to Datasets
• Free Spoken Digit Dataset (load_digits)
• Random Sequences (load_random_sequences)
• Dataset

Documentation Search and Index

• Search Page

• Index