TSInterpret: A Python Package for the Interpretability of Time Series Classification
Author:
Publisher
The Open Journal
Subject
General Earth and Planetary Sciences,General Environmental Science
Link
https://joss.theoj.org/papers/10.21105/joss.05220.pdf
Reference13 articles.
1. Scalable and accurate deep learning with electronic health records;Rajkomar;npj Digital Med,2018
2. Time-Series Classification Methods: Review and Applications to Power Systems Data;Susto;Big Data Application in Power Systems,2018
3. Deep learning for time series classification: A review;Fawaz;Data mining and knowledge discovery,2019
4. The great multivariate time series classification bake off: A review and experimental evaluation of recent algorithmic advances;Ruiz;Data Min Knowl Disc,2021
5. Benchmarking Deep Learning Interpretability in Time Series Predictions;Ismail;arXiv:2010.13924,2020
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1. XAI for Time Series Classification: Evaluating the Benefits of Model Inspection for End-Users;Communications in Computer and Information Science;2024
2. An Interactive Tool for Interpretability of Time Series Classification;Lecture Notes in Computer Science;2024
3. XTSC-Bench: Quantitative Benchmarking for Explainers on Time Series Classification;2023 International Conference on Machine Learning and Applications (ICMLA);2023-12-15
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