Multivariate Time Series Classification: A Deep Learning Approach

Multivariate Time Series Classification: A Deep Learning Approach. Abouelnaga, M., Vitay, J., & Farahani, A. July, 2023. arXiv:2307.02253 [cs]

Paper abstract bibtex

This paper investigates different methods and various neural network architectures applicable in the time series classification domain. The data is obtained from a fleet of gas sensors that measure and track quantities such as oxygen and sound. With the help of this data, we can detect events such as occupancy in a specific environment.

@misc{abouelnaga_multivariate_2023,
	title = {Multivariate {Time} {Series} {Classification}: {A} {Deep} {Learning} {Approach}},
	shorttitle = {Multivariate {Time} {Series} {Classification}},
	url = {http://arxiv.org/abs/2307.02253},
	abstract = {This paper investigates different methods and various neural network architectures applicable in the time series classification domain. The data is obtained from a fleet of gas sensors that measure and track quantities such as oxygen and sound. With the help of this data, we can detect events such as occupancy in a specific environment.},
	language = {en},
	urldate = {2024-05-06},
	publisher = {arXiv},
	author = {Abouelnaga, Mohamed and Vitay, Julien and Farahani, Aida},
	month = jul,
	year = {2023},
	note = {arXiv:2307.02253 [cs]},
	keywords = {Computer Science - Machine Learning, time-series segmentation},
}

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