Introducing the Attribution Stability Indicator: a Measure for Time Series XAI Attributions

1 code implementation • 6 Oct 2023 • Udo Schlegel, Daniel A. Keim

Given the increasing amount and general complexity of time series data in domains such as finance, weather forecasting, and healthcare, there is a growing need for state-of-the-art performance models that can provide interpretable insights into underlying patterns and relationships.

Time Series Time Series Classification +1

Visual Explanations with Attributions and Counterfactuals on Time Series Classification

no code implementations • 14 Jul 2023 • Udo Schlegel, Daniela Oelke, Daniel A. Keim, Mennatallah El-Assady

To further inspect the model decision-making as well as potential data errors, a what-if analysis facilitates hypothesis generation and verification on both the global and local levels.

Decision Making Explainable artificial intelligence +3

A Deep Dive into Perturbations as Evaluation Technique for Time Series XAI

1 code implementation • 11 Jul 2023 • Udo Schlegel, Daniel A. Keim

This paper provides an in-depth analysis of using perturbations to evaluate attributions extracted from time series models.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +4

ViNNPruner: Visual Interactive Pruning for Deep Learning

1 code implementation • 31 May 2022 • Udo Schlegel, Samuel Schiegg, Daniel A. Keim

In many cases, such large networks are not deployable on particular hardware and need to be reduced in size.

Time Series Model Attribution Visualizations as Explanations

no code implementations • 27 Sep 2021 • Udo Schlegel, Daniel A. Keim

We collect attribution heatmap visualizations and some alternatives, discuss the advantages as well as disadvantages and give a short position towards future opportunities for attributions and explanations for time series.

Position Time Series +1

TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models

1 code implementation • 17 Sep 2021 • Udo Schlegel, Duy Vo Lam, Daniel A. Keim, Daniel Seebacher

Time series forecasting is a demanding task ranging from weather to failure forecasting with black-box models achieving state-of-the-art performances.

Time Series Time Series Forecasting

An Empirical Study of Explainable AI Techniques on Deep Learning Models For Time Series Tasks

no code implementations • 8 Dec 2020 • Udo Schlegel, Daniela Oelke, Daniel A. Keim, Mennatallah El-Assady

Decision explanations of machine learning black-box models are often generated by applying Explainable AI (XAI) techniques.

BIG-bench Machine Learning Explainable Artificial Intelligence (XAI) +2

Towards a Rigorous Evaluation of XAI Methods on Time Series

no code implementations • 16 Sep 2019 • Udo Schlegel, Hiba Arnout, Mennatallah El-Assady, Daniela Oelke, Daniel A. Keim

In this work, we apply XAI methods previously used in the image and text-domain on time series.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +2

explAIner: A Visual Analytics Framework for Interactive and Explainable Machine Learning

1 code implementation • 29 Jul 2019 • Thilo Spinner, Udo Schlegel, Hanna Schäfer, Mennatallah El-Assady

We propose a framework for interactive and explainable machine learning that enables users to (1) understand machine learning models; (2) diagnose model limitations using different explainable AI methods; as well as (3) refine and optimize the models.

BIG-bench Machine Learning Explainable Artificial Intelligence (XAI)