What Makes PhasePoint Novel?
PhasePoint discovers latent temporal regimes directly from raw timestamps and exports two bundles: a model-ready feature matrix and a human-readable evidence pack. Features are interpretable, reproducible, and designed for plug-and-play use with any ML or forecasting model.
PhasePoint is not a model replacement and not a black-box AutoML system. It augments existing workflows by upgrading the time representation, without requiring model changes, and every exported artifact is traceable back to the underlying data and configuration.
Unlike traditional calendar flags and many legacy diagnostics approaches, PhasePoint uses phase-based segmentation across multiple cycles to identify regimes and transitions (e.g., zone boundaries, shift points). The result is time structure you can inspect, validate (stability/OOS), and operationalize as both features and diagnostics.
Data-driven
Reproducible outputs
Evidence-first
Model agnostic
Definitions and Technical Foundations
PhasePoint identifies latent temporal zones: persistent, data-supported time regimes embedded in timestamped series. It discovers structure across multiple timeframes and represents it with various feature types, capturing temporal effects that basic date-part features (day-of-week, month, holidays) and hand-crafted rules often miss.
PhasePoint exports two outputs: a model-ready feature matrix and a human-readable evidence bundle. The evidence bundle documents zone definitions, diagnostics, and run artifacts so results are reproducible, reviewable, and easy to integrate into existing forecasting or ML pipelines.
The core framework includes modules for time segmentation, stability/OOS validation, and artifact emission (reports, plots, and an optional run manifest). Each component is designed to be inspectable and operational in standard batch pipelines, with clear inputs/outputs and deterministic configuration.
