MorphIQ Wave / Signal analysis
Signal structure across time and scale.
From Haar and Daubechies wavelets to synchrosqueezed transforms: adaptive decomposition, denoising and cross-asset coherence for batch and streaming analysis.
- 6 classical · 6 adaptive
- 12transforms
- Haar through biorthogonal CDF
- 5wavelet families
- Analysis across time and scale
- Batch + stream
Capabilities
A focused responsibility. A clear interface.
Separate the scales
Decompose a time series into components that expose structure at different resolutions.
Build useful features
Apply denoising, adaptive methods and time-frequency analysis to research and model inputs.
Work with streams
Carry signal analysis into streaming workflows with explicit boundary and numerical behavior.
Methods and workflows
Explore the detail.
Explore the family
Related technologies
MorphIQ Analytics
Statistical rigor. Deep learning. Fast inference.
Statistical modeling and deep learning turn market data into estimates of regime, direction and relative volatility.
MorphIQ Markov
Hidden states. Observable evidence.
Hidden-state modeling, filtering, smoothing and parameter estimation for time series—with explicit convergence and numerical diagnostics.
MorphIQ Numerics
Verifiable precision. Reproducible results.
Correctly rounded core functions, machine-checked error bounds and SIMD kernels that preserve scalar results across supported platforms.