Streaming Wavelet Transforms for Time-Series ML
Build robust, low-latency features with streaming wavelets to improve denoising, regime detection, and model stability in time-series pipelines.
Stable Feature Engineering
MODWT-style transforms deliver shift-invariant features that reduce false correlations.
Streaming Ready
Incremental updates keep features synchronized with live data without batch recomputation.
Noise Reduction
Wavelet denoising improves signal-to-noise ratio, helping models converge faster and generalize better.
Where Wavelets Fit in ML Pipelines
Wavelets are most effective in preprocessing and feature engineering stages, where they can strip noise while preserving regime changes and transient signals.
For large-scale pipelines, streaming transforms reduce compute cost by updating only new samples, keeping feature windows aligned to real-time ingestion.
This makes wavelets a strong alternative to rolling FFT features, especially when low-latency feedback loops are required.
Deploy in Production
FAQ
Why use streaming wavelets for ML features?
Streaming wavelets provide stable, shift-invariant features that improve model convergence and reduce noise leakage.
How does this differ from rolling FFT features?
Wavelets maintain time localization without fixed windows, which reduces latency and preserves transient signals.
Which library should I use for ML pipelines?
VectorWave integrates with JVM-based pipelines (Spark/Flink), while IronWave is ideal for Rust-native ETL stacks.
Get Evaluation Access
Talk with our team about integrating wavelet analytics into your ML stack.