Anomaly detection in quasi-periodic energy consumption data series: a comparison of algorithms
Crossref DOI link: https://doi.org/10.1186/s42162-022-00230-7
Published Online: 2022-12-21
Update policy: https://doi.org/10.1007/springer_crossmark_policy
Zangrando, Niccolò
Fraternali, Piero
Petri, Marco
Pinciroli Vago, Nicolò Oreste
Herrera González, Sergio Luis
Text and Data Mining valid from 2022-12-21
Version of Record valid from 2022-12-21
Article History
First Online: 21 December 2022
Declaration
:
: The authors declare that they have no competing interests.