Interpretable Uncertainty Diagnostics
Building the tools to make probabilistic forecasts trustworthy, auditable, and actionable.
The Problem
Most forecasting models produce uncertainty estimates, but few practitioners know how to interpret them. Coverage gaps, overconfidence, and poor calibration go undetected — leading to decisions based on false confidence.
The Innovation
The k-diagram framework introduces a polar coordinate system for visualizing multiple dimensions of forecast quality simultaneously — coverage, calibration, severity, and reliability — in a single, interpretable chart.
A Concrete Application: Subsidence Forecasting
How k-diagram diagnostics improved real-world model evaluation
Generate Forecasts
Run XTFT or PINN-based model to produce probabilistic subsidence predictions with confidence intervals.
Apply k-Diagram
Feed predictions into the k-diagram toolkit to produce polar diagnostic plots across all coverage levels.
Interpret & Improve
Identify coverage gaps, overconfident intervals, or systematic biases and retrain accordingly.
What Can k-Diagram Answer?
The k-diagram toolkit turns abstract uncertainty metrics into visual diagnostics.
“Is my model well-calibrated?”
Visualize whether prediction intervals actually contain the true value at the stated confidence level.
“Where does coverage fail?”
Identify specific coverage levels or time horizons where the model systematically under- or over-predicts.
“How severe are the errors?”
Quantify the magnitude of coverage gaps and their potential impact on downstream decisions.
Impact
Key Research Outcomes
Papers in JOSS, Zenodo, and submissions to IJF and EMS — covering probabilistic diagnostics, CAS scoring, and spatiotemporal uncertainty.
k-diagram published in the Journal of Open Source Software (JOSS) 2025 — peer-reviewed software with full documentation and reproducible examples.
CAS (Cluster-Aware Scoring) and k-diagram diagnostics — novel evaluation frameworks for assessing probabilistic forecast quality.
Visualization functions covering bias, sharpness, calibration, and reliability — a comprehensive toolkit for forecast uncertainty analysis.
Publications
Related Work
Machine learning-based techniques for land subsidence simulation in an urban area
Liu, J.; Liu, W.; Allechy, F. B.; Zheng, Z.; Liu, R.; Kouadio, K. L.*
A Diagnostic Framework for Spatiotemporal Forecast Uncertainty
Kouadio, K. L.; Liu, R.; Loukou, K. G. H.; Liu, W.; Qing, Z.; Liu, Z.
CAS: Cluster-Aware Scoring for Probabilistic Forecasts
Kouadio, K. L.; Liu, R.
k-diagram: Rethinking Forecasting Uncertainty via Polar-Based Visualization
Kouadio, K. L.
k-diagram: Technical Report — Derivations and Details
Kouadio, K. L.
XTFT: A Next-Generation Temporal Fusion Transformer for Uncertainty-Rich Time Series Forecasting
Kouadio, K. L.; Liu, Z.; Liu, R.; Bizimana, P. C.; Yang, G.; Liu, W.