Solar flare prediction with temporal convolutional networks

dc.contributor.author	Krynauw, Dewald D.
dc.contributor.author	Davel, Marelie H.
dc.contributor.author	Lotz, Stefan
dc.date.accessioned	2020-01-27T13:35:35Z
dc.date.available	2020-01-27T13:35:35Z
dc.date.issued	2019-12
dc.description.abstract	Sequences are typically modelled with recurrent architectures, but growing research is finding convolutional architectures to also work well for sequence modelling [1]. We explore the performance of Temporal Convolutional Networks (TCNs) when applied to an important sequence modelling task: solar flare prediction. We take this approach, as our future goal is to apply techniques developed for probing and interpreting general convolutional neural networks (CNNs) to solar flare prediction.	en_US
dc.identifier.citation	Dewald D. Krynauw, Marelie H. Davel and Stefan Lotz, “Solar flare prediction with temporal convolutional networks“, In Proc. South African Forum for Artificial Intelligence Research (FAIR2019) (Work in Progress), Cape Town, South Africa, December 2019.	en_US
dc.identifier.issn	1613-0073
dc.identifier.uri	http://hdl.handle.net/10394/33959
dc.language.iso	en	en_US
dc.publisher	In Proc. South African Forum for Artificial Intelligence Research (FAIR2019)	en_US
dc.subject	Solar flare	en_US
dc.subject	prediction with temporal convolutional networks	en_US
dc.title	Solar flare prediction with temporal convolutional networks	en_US
dc.type	Other	en_US

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