Generalized performance of LSTM in time-series forecasting
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Author (Corporation)
Publication date
2024
Typ of student thesis
Course of study
Collections
Type
01A - Journal article
Editors
Editor (Corporation)
Supervisor
Parent work
Applied Artificial Intelligence
Special issue
DOI of the original publication
Link
Series
Series number
Volume
38
Issue / Number
1
Pages / Duration
1-28
Patent number
Publisher / Publishing institution
Taylor & Francis
Place of publication / Event location
London
Edition
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Programming language
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Practice partner / Client
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ISBN
ISSN
1087-6545
0883-9514
0883-9514
Language
English
Created during FHNW affiliation
Yes
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Gold
License
Citation
Prater, R., Hanne, T., & Dornberger, R. (2024). Generalized performance of LSTM in time-series forecasting. Applied Artificial Intelligence, 38(1), 1–28. https://doi.org/10.1080/08839514.2024.2377510