Singular Spectrum Analysis — non-parametric trend extraction from noisy data

Singular Spectrum Analysis — non-parametric trend extraction from noisy data

SSA decomposes time-series into trend, cycles, and noise without assuming a fixed functional form. Breaks data into reconstructed components (RC 1+7 trend, RC 2+3 annual, RC 9 high-freq). DM-bait for code repo.

• SSA extracts trends without assuming functional form • Separates signal components by eigenvalue decomposition • Robust alternative to Fourier for non-stationary data

Python Non-Parametric Decomposition, Singular Spectrum Analysis, Trend Extraction

Stop guessing, start decomposing. 📈 ​Is your time-series data buried under noise? Singular Spectrum Analysis (SSA) is one of the most robust non-parametric methods for extracting trends and periodicities without a fixed functional form. Comment "SSA" and I’ll DM you the full repository for free! 💻 ​In this visualization, we break down global mass variations into: ​Long-term trends (RC 1+7) ​Annual & Semi-annual cycles (RC 2+3, 4+6+8) ​High-frequency signals (RC 9) ​By comparing major data centers like CSR, GFZ, and JPL, we can see the incredible consistency in the reconstructed components. #quantfinance #econophysics #algorithmictrading #math #python quant.dhawan   21w Comment "SSA" and I’ll DM you the full repository for free! 💻