# Timeseries-aware Uncertainty Wrappers for Uncertainty Quantification of Information-Fusion-Enhanced AI Models based on Machine Learning

arXiv: https://arxiv.org/abs/2305.14872 · HTML: https://mlsanity.com/paper/2305.14872/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 38 references that are in the map.
- First posted on arXiv 24 May 2023.

## Builds on (most cited first, 5 of 5)

- [Neural Simplex Architecture](https://mlsanity.com/paper/1908.00528/) (2019)
- [An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Software](https://mlsanity.com/paper/1709.02435/) (2017)
- [Towards a Common Testing Terminology for Software Engineering and Data Science Experts](https://mlsanity.com/paper/2108.13837/) (2021)
- [Towards Identifying and Managing Sources of Uncertainty in AI and Machine Learning Models - An Overview](https://mlsanity.com/paper/1811.11669/) (2018)
- [Architectural patterns for handling runtime uncertainty of data-driven models in safety-critical perception](https://mlsanity.com/paper/2206.06838/) (2022)

## Built on by (most cited first, 1 of 1)

- [Uncertainty Wrapper in the medical domain: Establishing transparent uncertainty quantification for opaque machine learning models in practice](https://mlsanity.com/paper/2311.05245/) (2023)

## Abstract (arXiv)

As the use of Artificial Intelligence (AI) components in cyber-physical systems is becoming more common, the need for reliable system architectures arises. While data-driven models excel at perception tasks, model outcomes are usually not dependable enough for safety-critical applications. In this work,we present a timeseries-aware uncertainty wrapper for dependable uncertainty estimates on timeseries data. The uncertainty wrapper is applied in combination with information fusion over successive model predictions in time. The application of the uncertainty wrapper is demonstrated with a traffic sign recognition use case. We show that it is possible to increase model accuracy through information fusion and additionally increase the quality of uncertainty estimates through timeseries-aware input quality features.