Toward Predictive Anomalous Systems Analysis (pASA): Domain Trends, Drift Forecasting, Cascade Modeling, and Spatial Probability in the Unified Archetypal Bayesian Theory
Year: 2025
Type: preprint
Abstract: Through analytical examples, mathematically grounded proofs-of-concept, and multi-layered integration of NOAA, NASA, USGS, anthropological, and institutional datasets, this preprint demonstrates that ASA is capable of meaningful forward-looking inference at multiple scales—from regional archaeological likelihoods to cross-domain activation sequences. Predictive ASA moves anomaly science from classification into early-stage forecasting. It does not predict discrete events with absolute specificity, but it does generate statistically significant foresight about event classes, domain activation cycles, and spatiotemporal APC likelihood fields. This capability is not speculative; it is built directly upon the data structures, equations, ecosystem, and methodology already validated within ASA.
Author: Laing, Zachariah
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7108340352