S3-Algorithm: Deterministic Structure Detection in High-Noise Time Series via a Minimal Structural Invariant
Abstract
This paper introduces the S3-Algorithm, a deterministic streaming method for detecting structural transitions in high-noise time series using discrete step geometry and a recursive memory state. In a controlled Shift Table benchmark (N=400) with injected high-frequency noise, S3 achieves 93.5% accuracy (374/400) with parameters alpha=0.85 and theta=0.85. The method is demonstrated on Swift/XRT GRB data (GRB 060729), where the afterglow tail spans multiple orders of magnitude.
Fields of Study & Subjects
Time Series Analysis and Forecasting
Anomaly Detection Techniques and Applications
Complex Systems and Time Series Analysis
Publication details
- Journal / source
- Zenodo (CERN European Organization for Nuclear Research)
- Publication date
- 26 February 2026
- Language
- EN
- Document type
- Preprint