Algorithms
SAX-VSM and its building blocks — z-normalization, PAA, SAX, tf·idf and cosine similarity — built up step by step.
This module builds SAX-VSM from the ground up. The pages are ordered: each algorithm below builds on the previous ones, ending with the full classifier and a bonus visualization technique.
Z-normalization
Why and how time series are standardized to zero mean and unit variance before discretization.
readingPiecewise Aggregate Approximation (PAA)
Dimensionality reduction by frame averaging, and the lower-bounding PAA distance.
readingSymbolic Aggregate Approximation (SAX)
How SAX turns a real-valued time series into a string: PAA frames, Gaussian breakpoints, and the lower-bounding MINDIST — with a fully worked example.
readingTime series discretization via sliding window
How a sliding window turns one long time series into an ordered sequence of SAX words describing its local features.
readingSAX numerosity reduction
Collapsing runs of identical consecutive SAX words — smaller output, faster algorithms, and naturally variable-length patterns.
readingtf·idf statistics
How term frequency and inverse document frequency turn per-class bags of SAX words into class-characteristic weight vectors.
readingCosine similarity
The angle-based similarity measure that scores an unlabeled time series against each class's tf·idf weight vector.
readingInterpretable time series classification with SAX-VSM
The full SAX-VSM algorithm: per-class bags of SAX words, tf·idf weighting, and cosine-similarity classification.
readingSAX bitmap patterns visualization
Turning SAX word frequencies into color bitmaps for at-a-glance comparison of large time series collections.