A hybrid SARIMAX-LSTM model optimised by ANN for near-term forecasting: An application to China’s Natural Gas consumption

Sung, J Shi, X Teske, S Li, M

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Field	Value	Language
dc.contributor.author	Sung, J
dc.contributor.author	Shi, X
dc.contributor.author	Teske, S
dc.contributor.author	Li, M https://orcid.org/0000-0002-8807-7277
dc.date.accessioned	2025-06-23T23:50:16Z
dc.date.available	2025-06-23T23:50:16Z
dc.date.issued	2025-06-24
dc.identifier.citation	2025
dc.identifier.uri	http://hdl.handle.net/10453/187869
dc.language	en
dc.rights	info:eu-repo/semantics/openAccess
dc.title	A hybrid SARIMAX-LSTM model optimised by ANN for near-term forecasting: An application to China’s Natural Gas consumption
dc.type	preprint
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Business School
pubs.organisational-group	University of Technology Sydney/Business School/Economics Discipline
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Climate Risk and Resilience (CCRR)
utslib.copyright.status	open_access	*
dc.date.updated	2025-06-23T23:50:15Z

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