ATARS v2.0 — Automated Time-Series Analysis and Reporting System

ATARS v2.0 — Automated Time-Series Analysis and Reporting System ATARS is a formally specified, single-file Python pipeline that transforms raw environmental time-series CSV data into a complete, reproducible analysis package — an 18-section Word report, 15 publication-ready charts, and a SHA-256 verified audit log — automatically, with no manual steps. The system implements 16 formally numbered mathematical operators covering daily aggregation, rolling baseline computation, z-score anomaly detection, delta deviation, confidence intervals, ACF/PACF temporal analysis, Pearson correlation, and OLS regression. Two additive machine learning modules extend the formal layer: Isolation Forest for multivariate anomaly detection and Holt-Winters triple exponential smoothing for 14-day forecasting with 80% prediction intervals. The primary novel contribution is the Runtime Grounding Verifier (RGV) — an algorithmic system that enforces and measures LLM numerical grounding against a formally defined JSON data contract J. Every numerical claim in AI-generated narrative is matched against J using type-adaptive tolerance, producing a scalar G_rate metric saved in the audit log. This is the first implementation of runtime programmatic numerical grounding enforcement inside an automated scientific reporting pipeline. Validated on real Gurugram CPCB 2024 air quality data (61,320 hourly records, 7 variables). G_rate achieved: 92.9% (PASS). Isolation Forest detected 16 multivariate anomaly days missed by univariate z-score analysis. Keywords: air quality analysis, time-series, anomaly detection, LLM grounding verification, Isolation Forest, Holt-Winters forecasting, automated reporting, environmental monitoring, reproducible research