Built at Data Sovereignty & Responsible AI · submitted 20 September 2026
A. A general-purpose statistical analysis pipeline for numeric, multi-run tabular data. TPM takes raw datasets, builds a statistical baseline, detects changes in new data, and uses an LLM to help explain what the analysis may mean. B. Key Features 1. Automatic dataset discovery — detects delimiters, timestamps, labels, run IDs, sample IDs, and numeric columns. 2. Statistical profiling — uses ydata-profiling for per-run statistics, correlations, time-series diagnostics, and quality alerts. 3. Causal discovery — uses Tigramite PCMCI with partial correlation tests. 4. Automatic clustering — groups related variables using hierarchical clustering and silhouette-based cluster selection. 5. Multi-perspective anomaly detection — checks mean shifts, variance shifts, correlation changes, and fault onset. 6. Autocorrelation-aware statistics — adjusts effective sample size for sequential data. 7. CUSUM fault detection — identifies when changes begin and provides a possible propagation order. 8. LLM-assisted interpretation — explains variables, cluster roles, and possible root causes from statistical evidence. 9. Data-quality rules — supports built-in checks and human-confirmed custom rules. 10. Audit logging — records LLM calls and important system decisions. 11. Reusable reports and plots — exports structured JSON/CSV data plus a readable report.md. 12. Web dashboard — inspect results and upload new runs through a FastAPI/Jinja2 interface. 13. Dataset-agnostic design — not tied to the original Tennessee Eastman dataset.
