Research topic
wavelets
How to Evaluate a Market Regime Detector Without Fooling Yourself
A detector can look fast simply because it changes its mind constantly. This held-out synthetic benchmark shows why regime research needs ground truth, causal features, strong baselines, and a scorecard that keeps latency beside stability.
Advanced Signal Decompositions Need the Right Test
SWT, synchrosqueezing, EMD, VMD, and EWT encode different assumptions. Four synthetic diagnostics show what each method earns, where a direct baseline still wins, and why one leaderboard cannot answer every regime question.
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.
Wavelet-Domain Regime Detection with the MODWT
Markets change character at different speeds. The MODWT splits a return series into scale components whose variances add up exactly — which turns a vague question about regimes into an estimable one.