timeXplain - A Framework for Explaining the Predictions of Time Series Classifiers.

Felix Mujkanovic, Vanja Doskoč, Martin Schirneck, Patrick Schäfer, Tobias Friedrich

Type: Preprint

Publication Date: 2020-07-15

Citations: 6

Locations

arXiv (Cornell University) - View

Similar Works

Action	Title	Year	Authors
+	timeXplain -- A Framework for Explaining the Predictions of Time Series Classifiers	2020	Felix Mujkanovic Vanja Doskoč Martin Schirneck Patrick Schäfer Tobias Friedrich
+	EAMDrift: An interpretable self retrain model for time series	2023	Gonçalo Mateus Cláudia Soares João Leitão António Rodrigues
+ PDF Chat	Time is Not Enough: Time-Frequency based Explanation for Time-Series Black-Box Models	2024	Hyunseung Chung Sumin Jo Youngcheol Kwon Edward Choi
+	XAI Methods for Neural Time Series Classification: A Brief Review	2021	Ilija Šimić Vedran Sabol Eduardo Veas
+	Explainable Multivariate Time Series Classification: A Deep Neural Network Which Learns To Attend To Important Variables As Well As Informative Time Intervals	2020	Tsung-Yu Hsieh Suhang Wang Yiwei Sun Vasant Honavar
+ PDF Chat	Foundation Models for Time Series Analysis: A Tutorial and Survey	2024	Yuxuan Liang Haomin Wen Yuqi Nie Yushan Jiang Ming Jin Dongjin Song Shirui Pan Qingsong Wen
+ PDF Chat	Foundation Models for Time Series Analysis: A Tutorial and Survey	2024	Yuxuan Liang Haomin Wen Yuqi Nie Yushan Jiang Ming Jin Dongjin Song Shirui Pan Qingsong Wen
+	Time Series Regression.	2020	Chang Wei Tan Christoph Bergmeir François Petitjean Geoffrey I. Webb
+	XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification	2023	Jacqueline Höllig Steffen Thoma Florian Grimm
+ PDF Chat	XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification	2023	Jacqueline Höllig Steffen Thoma Florian Grimm
+ PDF Chat	Towards Generalisable Time Series Understanding Across Domains	2024	Özgün Turgut Philip Müller Martin J. Menten Daniel Rueckert
+	sktime: A Unified Interface for Machine Learning with Time Series	2019	Markus Löning Anthony Bagnall Sajaysurya Ganesh Viktor Kazakov Jason Lines Franz J. Király
+	TimeGPT-1	2023	Azul Garza Max Mergenthaler-Canseco
+	Inherently Interpretable Time Series Classification via Multiple Instance Learning	2023	Joseph Early Gavin Cheung Kurt Cutajar Hanting Xie Jas Kandola Niall Twomey
+	PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification	2021	Dominique Mercier Andreas Dengel Sheraz Ahmed
+ PDF Chat	PatchX: Explaining Deep Models by Intelligible Pattern Patches for Time-series Classification	2021	Dominique Mercier Andreas Dengel Sheraz Ahmed
+ PDF Chat	A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting	2024	Pierre-Daniel Arsenault Shengrui Wang Jean-Marc Patenande
+ PDF Chat	Improving the Evaluation and Actionability of Explanation Methods for Multivariate Time Series Classification	2024	Davide Italo Serramazza Thach Le Nguyen Georgiana Ifrim
+ PDF Chat	Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification	2024	Qi Huang Wei Chen Thomas Bäck Bas van Stein
+ PDF Chat	Can time series forecasting be automated? A benchmark and analysis	2024	Anvitha Thirthapura Sreedhara Joaquin Vanschoren

Works That Cite This (2)

Action	Title	Year	Authors
+ PDF Chat	TS-MULE: Local Interpretable Model-Agnostic Explanations for Time Series Forecast Models	2021	Udo Schlegel Duy Lam Vo Daniel A. Keim Daniel Seebacher
+	Time Series Model Attribution Visualizations as Explanations	2021	Udo Schlegel Daniel A. Keim

Works Cited by This (10)

Action	Title	Year	Authors
+ PDF Chat	European Union Regulations on Algorithmic Decision Making and a “Right to Explanation”	2017	Bryce Goodman Seth Flaxman
+	Towards robust interpretability with self-explaining neural networks	2018	David Alvarez-Melis Tommi Jaakkola
+	Interpretable Time Series Classification using All-Subsequence Learning and Symbolic Representations in Time and Frequency Domains.	2018	Thach Le Nguyen Severin Gsponer Iulia Ilie Georgiana Ifrim
+	Deep learning for time series classification: a review	2019	Hassan Ismail Fawaz Germain Forestier Jonathan Weber Lhassane Idoumghar Pierre-Alain Müller
+ PDF Chat	The mythos of model interpretability	2018	Zachary C. Lipton
+	A Unified Approach to Interpreting Model Predictions	2017	Scott Lundberg Su‐In Lee
+	sktime: A Unified Interface for Machine Learning with Time Series	2019	Markus Löning Anthony Bagnall Sajaysurya Ganesh Viktor Kazakov Jason Lines Franz J. Király
+ PDF Chat	Techniques for interpretable machine learning	2019	Mengnan Du Ninghao Liu Xia Hu
+ PDF Chat	Fast and Accurate Time Series Classification with WEASEL	2017	Patrick Schäfer Ulf Leser
+ PDF Chat	Scalable and accurate deep learning with electronic health records	2018	Alvin Rajkomar Eyal Oren Kai Chen Andrew M. Dai Nissan Hajaj Michaela Hardt Peter J. Liu Xiaobing Liu Jake Marcus Mimi Sun