tsseg.algorithms.vsax package

VSAX — Variable-length SAX state detection.

Description

VSAX converts each channel to Symbolic Aggregate approXimation (SAX) symbols, then finds the variable-length segmentation that minimises PAA reconstruction error plus an additive penalty per segment. The pipeline:

1. Z-normalisation — optionally standardise each channel.

2. PAA — reduce each candidate segment to paa_segments frames.

3. SAX — discretise PAA values into alphabet_size symbols using Gaussian breakpoints (or adaptive empirical quantiles).

4. DP segmentation — dynamic programming over num_lengths candidate segment lengths minimises reconstruction error + penalty per segment.

5. Symbol merging — per-channel SAX symbol tuples are clustered via agglomerative clustering on Hamming distance (threshold symbol_merge_threshold).

Type: state detection

Supervision: semi-supervised or unsupervised

Scope: univariate

Parameters

Name	Type	Default	Description
alphabet_size	int	6	Number of SAX symbols per channel.
paa_segments	int	8	Number of PAA frames per segment.
min_segment_length	int	20	Minimum admissible segment length.
max_segment_length	int	180	Maximum admissible segment length.
num_lengths	int	6	Number of candidate lengths (linearly spaced min..max).
penalty	float	0.8	Cost per new segment. Larger values produce longer segments.
symbol_merge_threshold	float	0.2	Normalised Hamming distance threshold for merging symbols. 0 = exact match only, 1 = single global state.
zscore	bool	True	Apply per-channel z-normalisation.
adaptive_breakpoints	bool	True	Learn SAX breakpoints from empirical quantiles.
axis	int	0	Time axis.

Usage

from tsseg.algorithms import VSAXDetector

detector = VSAXDetector(
    alphabet_size=8, penalty=1.0, min_segment_length=30) states =
detector.fit_predict(X)

Implementation: Origin: new code.

Reference: —

Submodules

tsseg.algorithms.vsax.detector module

Module contents