Uncertain Time Series Classification

Mbouopda, Michael Franklin

doi:10.24963/ijcai.2021/683

Uncertain Time Series Classification

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Doctoral Consortium. Pages 4903-4904.

https://doi.org/10.24963/ijcai.2021/683

Keywords

Machine Learning: Time-series; Data Streams
Machine Learning: Classification
Machine Learning: Explainable/Interpretable Machine Learning

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.

Michael Franklin Mbouopda. 2021. Uncertain Time Series Classification. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 4903–4904. https://doi.org/10.24963/ijcai.2021/683

@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    = {Prof. 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},
}

TY  - CPAPER
TI  - Uncertain Time Series Classification
AU  - Mbouopda, Michael Franklin
T2  - Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2021
SP  - 4903
EP  - 4904
DO  - 10.24963/ijcai.2021/683
UR  - https://doi.org/10.24963/ijcai.2021/683
ER  -