TSFeatLIME: An Online User Study in Enhancing Explainability in
Univariate Time Series Forecasting

TSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecasting

24 September 2024

Kevin McAreavey

Weiru Liu

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Papers citing "TSFeatLIME: An Online User Study in Enhancing Explainability in Univariate Time Series Forecasting"

4 / 4 papers shown

Title
TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models U. Schlegel D. Lam Daniel A. Keim Daniel Seebacher FAtt AI4TS 107 32 0 17 Sep 2021
Explainable Artificial Intelligence (XAI) on TimeSeries Data: A Survey Thomas Rojat Raphael Puget David Filliat Javier Del Ser R. Gelin Natalia Díaz Rodríguez XAI AI4TS 99 135 0 02 Apr 2021
One Explanation Does Not Fit All: A Toolkit and Taxonomy of AI Explainability Techniques Vijay Arya Rachel K. E. Bellamy Pin-Yu Chen Amit Dhurandhar Michael Hind ... Karthikeyan Shanmugam Moninder Singh Kush R. Varshney Dennis L. Wei Yunfeng Zhang XAI 74 393 0 06 Sep 2019
"Why Should I Trust You?": Explaining the Predictions of Any Classifier Marco Tulio Ribeiro Sameer Singh Carlos Guestrin FAtt FaML 1.2K 17,092 0 16 Feb 2016

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