MorphIQ Labs ·
Wavelet Feature Extraction for Time-Series ML Pipelines
Wavelet transforms are useful before prediction begins: they turn non-stationary time series into scale-aware features that can feed ML and AI pipelines without hiding the time structure.
Time-series ML fails quietly when the feature layer ignores scale. A price series, volatility series, or sensor stream may look like one signal, but its behavior is usually a mixture of fast noise, medium-horizon structure, and slow drift. A single rolling statistic compresses those pieces into one number.
Wavelet feature extraction keeps the scale information.
What the transform contributes
A wavelet transform decomposes a signal into localized scale bands. For a model pipeline, that gives you features such as:
- Energy by scale.
- Recent changes in high-frequency versus low-frequency activity.
- Coefficients aligned to the event time.
- Denoised reconstructions.
- Jump or discontinuity indicators.
- Multi-scale variance and coherence measures.
Those features are useful because they answer questions a model can consume: which horizon is active, whether the signal is smooth or jumpy, and whether a regime is changing at the fast scales or the slow scales.
Why implementation quality matters
Wavelet features are easy to prototype and easy to get subtly wrong. Boundary handling, filter conventions, level selection, reconstruction identity, and floating-point behavior all affect what the features mean.
That is why FerroWave treats transform identity and reference equivalence as part of the product surface. The public verification page separates the proven transform laws from measured floating-point behavior and implementation tests. The point is not to claim that feature extraction makes an ML model correct. The point is to make the feature layer inspectable and reproducible.
Where this fits
In the MorphIQ stack, FerroWave can support market-regime work, volatility texture, denoising, jump detection, and ML-ready time-series feature pipelines. The same primitive can serve a trading workflow, a research notebook, or a production feature service, as long as the assumptions and limits are kept with the features.
See the public FerroWave verification surface: wave.morphiqlabs.com/verification.