ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2307.05104
  4. Cited By
A Deep Dive into Perturbations as Evaluation Technique for Time Series
  XAI

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

11 July 2023
U. Schlegel
Daniel A. Keim
    AAML
    AI4TS
ArXivPDFHTML

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

3 / 3 papers shown
Title
Class-Dependent Perturbation Effects in Evaluating Time Series Attributions
Class-Dependent Perturbation Effects in Evaluating Time Series Attributions
Gregor Baer
Isel Grau
Chao Zhang
Pieter Van Gorp
AAML
41
0
0
24 Feb 2025
Time Series Model Attribution Visualizations as Explanations
Time Series Model Attribution Visualizations as Explanations
U. Schlegel
Daniel A. Keim
TDI
BDL
FAtt
AI4TS
XAI
31
14
0
27 Sep 2021
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series
  Forecast Models
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models
U. Schlegel
D. Lam
Daniel A. Keim
Daniel Seebacher
FAtt
AI4TS
32
27
0
17 Sep 2021
1