Industrial AI Robustness Card for Time Series Models

TL;DR

This paper presents the Industrial AI Robustness Card for Time Series (IARC-TS), a protocol for documenting and evaluating the robustness of industrial time series models, aligning with emerging regulations.

Contribution

It introduces a lightweight, empirical protocol that combines drift detection, uncertainty quantification, and stress testing for industrial time series models.

Findings

Supports reproducible robustness evidence in biopharmaceutical soft sensors

Maps robustness measures to EU AI Act obligations

Provides a practical protocol for robustness evaluation

Abstract

Industrial AI practitioners face vague robustness requirements in emerging regulations and standards but lack concrete, implementation-ready protocols. This paper introduces the Industrial AI Robustness Card for Time Series (IARC-TS), a lightweight protocol for documenting and evaluating industrial time series models. IARC-TS specifies required fields and an empirical measurement and reporting protocol that combines drift and operational domain monitoring, uncertainty quantification, and stress tests, and maps these to selected EU AI Act documentation, testing, and monitoring obligations. A biopharmaceutical soft sensor case study illustrates how IARC-TS supports reproducible robustness evidence and defines monitoring triggers.