Closed-Form Complex-Domain Encoding vs Learned Neural Encoders for Time-Series Trajectory Data: Lossless Reconstruction, Output Determinism, Compute Efficiency, and Calibration-Free Forecasting — Supplementary Reproducibility Archive

Supplementary reproducibility archive for Paper 5 of the spiral-domain encoder validation campaign, targeting IEEE Transactions on Neural Networks and Learning Systems (manuscript in preparation).Coverage. 4 pre-registered studies (Studies 63–66, Phase XI of the spiral-domain encoder validation campaign). 12 hypotheses, 5 SUPPORTED (42%), 7 honest bounded negatives substantively interpreted (two of which revealed stronger architectural findings than the literal hypotheses tested).Substantive findings. 7 orders of magnitude lower encode-decode RMSE than matched autoencoder (5.4×10⁻⁸ vs 0.13); 32 orders of magnitude lower output variance than VAE (1.05×10⁻³² vs 0.885); 117× faster inference vs transformer; 2,589× fewer parameters; 30× better small-data forecast RMSE; zero training epochs (instant cold-start); AR(1) near-optimal for spiral-encoded trajectories (learned heads make forecasting worse).Contents.• README.md — submission-package map and reproduction instructions• preregistrations/ — frozen pre-registration .md documents with literal-threshold decision rules• reports/ — per-study .md verdict reports against frozen rules + phase summaries• runners/ — deterministic Python runners (PYTHONHASHSEED=0) reproducing every reported measurement• raw_data/ — per-study CSV outputs and machine-readable JSON verdict blocks• figures/ — manuscript figures (PNG, 300 DPI) + figure-build Python script• code/ — encoder source code (spiral.py), supporting modules (baselines.py, data.py), and package __init__ filesReproducibility. Full validation pipeline is reproducible end-to-end under PYTHONHASHSEED=0 on a standard Python 3.9+ installation with NumPy 2.0+, SciPy, scikit-learn, and PyTorch 2.8+ (required for the autoencoder, variational autoencoder, and transformer baselines). For full reproducibility of the determinism measurements, configure torch.use_deterministic_algorithms(True) and run on CPU. Reference machine: Apple Silicon arm64 (M-series), macOS 14.Methodological discipline. Every hypothesis was pre-registered with externally anchored decision rules frozen prior to runner execution. Zero post-hoc threshold adjustments were applied. Honest bounded negatives are interpreted substantively rather than discarded.Related companion archives. Paper 1 (10.5281/zenodo.20129137), Paper 2 (10.5281/zenodo.20138786), Paper 3 (10.5281/zenodo.20139171), and the corresponding Papers 4, 6, 7 archives in this same Zenodo collection.