IJCAI 2021poster4 citations
Uncertain Time Series Classification
Abstract
Time series analysis has gained a lot of interest during the last decade with diverse applications in a large range of domains such as medicine, physic, and industry. The field of time series classification has been particularly active recently with the development of more and more efficient methods. However, the existing methods assume that the input time series is free of uncertainty. However, there are applications in which uncertainty is so important that it can not be neglected. This project aims to build efficient, robust, and interpretable classification methods for uncertain time series.
Machine Learning: Time-seriesData StreamsMachine Learning: ClassificationMachine Learning: Explainable/Interpretable Machine Learning
BibTeX
@inproceedings{ijcai2021p683,
title = {Uncertain Time Series Classification},
author = {Mbouopda, Michael Franklin},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI-21}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Zhi-Hua Zhou},
pages = {4903--4904},
year = {2021},
month = {8},
note = {Doctoral Consortium},
doi = {10.24963/ijcai.2021/683},
url = {https://doi.org/10.24963/ijcai.2021/683},
}