time-series feature extraction Rust
Time-series feature extraction in Rust.
MorphIQ Wave gives time-series systems a Rust feature layer for multi-scale behavior, denoising, regime changes, and online inference.
Problem
Why this needs production signal processing
Feature pipelines often split between Python research and separate production code.
Simple rolling statistics miss localized jumps, regime shifts, and scale-specific variance.
Production systems need features that are repeatable, inspectable, and fast.
Workflow
How MorphIQ Wave handles it
Choose a transform
Pick a wavelet, packet, continuous, or adaptive decomposition based on the signal behavior.
Summarize by scale
Generate features from coefficients, energy bands, modes, ridges, or denoised reconstructions.
Feed the model
Use the features in classical models, CNN inputs, streaming inference, or downstream analytics.
Fit
What makes this MorphIQ Wave-shaped
MorphIQ Wave includes classical and adaptive transform families.
Streaming performance is part of the public benchmark story.
AI/ML feature use is first-class site copy, not an afterthought.
Questions
Common concerns
Why wavelets for time-series features?
Wavelets expose local behavior at multiple scales, which can be useful when signals are non-stationary or noisy.
Does MorphIQ Wave require Python at runtime?
No. The library is positioned as pure Rust while staying pinned against Python reference behavior where appropriate.
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