wavelet feature extraction
Wavelet feature extraction for ML pipelines.
MorphIQ Wave turns wavelet decompositions into features that can move from research notebooks to Rust inference and analytics systems.
Problem
Why this needs production signal processing
Time-domain features miss scale-localized behavior that matters in noisy signals.
Notebook feature code often breaks when moved into lower-latency production systems.
ML pipelines need repeatable feature generation across research and deployment environments.
Workflow
How MorphIQ Wave handles it
Decompose the signal
Classical or adaptive transforms expose behavior across time, scale, frequency, and mode.
Generate features
Feature vectors, scaleograms, coefficients, energies, ridges, and streaming summaries can feed downstream models.
Deploy in Rust
The same transform path can support batch research and production feature extraction.
Fit
What makes this MorphIQ Wave-shaped
The public site explicitly highlights AI and ML feature extraction.
MorphIQ Wave supports multi-scale vectors, scaleogram tensors, and streaming features.
Reference-pinned behavior helps preserve research-to-runtime equivalence.
Questions
Common concerns
Are wavelet features only for financial data?
No. Financial signals are one use case; the feature-extraction story applies to broader time-series ML pipelines.
Can features be generated online?
Yes. Streaming transforms and online inference features are part of the public positioning.
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